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Pimenta, Mayra Mercedes Zegarra. "Self-organization map in complex network." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-30102018-111955/.
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The Self-Organization Map (SOM) is an artificial neural network that was proposed as a tool for exploratory analysis in large dimensionality data sets, being used efficiently for data mining. One of the main topics of research in this area is related to data clustering applications. Several algorithms have been developed to perform clustering in data sets. However, the accuracy of these algorithms is data depending. This thesis is mainly dedicated to the investigation of the SOM from two different approaches: (i) data mining and (ii) complex networks. From the data mining point of view, we analyzed how the performance of the algorithm is related to the distribution of data properties. It was verified the accuracy of the algorithm based on the configuration of the parameters. Likewise, this thesis shows a comparative analysis between the SOM network and other clustering methods. The results revealed that in random configuration of parameters the SOM algorithm tends to improve its acuracy when the number of classes is small. It was also observed that when considering the default configurations of the adopted methods, the spectral approach usually outperformed the other clustering algorithms. Regarding the complex networks approach, we observed that the network structure has a fundamental influence of the algorithm accuracy. We evaluated the cases at short and middle learning time scales and three different datasets. Furthermore, we show how different topologies also affect the self-organization of the topographic map of SOM network. The self-organization of the network was studied through the partitioning of the map in groups or communities. It was used four topological measures to quantify the structure of the groups such as: modularity, number of elements per group, number of groups per map, size of the largest group in three network models. In small-world (SW) networks, the groups become denser as time increases. An opposite behavior is found in the assortative networks. Finally, we verified that if some perturbation is included in the system, like a rewiring in a SW network and the deactivation model, the system cannot be organized again. Our results enable a better understanding of SOM in terms of parameters and network structure.<br>Um Mapa Auto-organizativo (da sigla SOM, Self-organized map, em inglês) é uma rede neural artificial que foi proposta como uma ferramenta para análise exploratória em conjuntos de dados de grande dimensionalidade, sendo utilizada de forma eficiente na mineração de dados. Um dos principais tópicos de pesquisa nesta área está relacionado com as aplicações de agrupamento de dados. Vários algoritmos foram desenvolvidos para realizar agrupamento de dados, tendo cada um destes algoritmos uma acurácia específica para determinados tipos de dados. Esta tese tem por objetivo principal analisar a rede SOM a partir de duas abordagens diferentes: mineração de dados e redes complexas. Pela abordagem de mineração de dados, analisou-se como o desempenho do algoritmo está relacionado à distribuição ou características dos dados. Verificou-se a acurácia do algoritmo com base na configuração dos parâmetros. Da mesma forma, esta tese mostra uma análise comparativa entre a rede SOM e outros métodos de agrupamento. Os resultados revelaram que o uso de valores aleatórios nos parâmetros de configuração do algoritmo SOM tende a melhorar sua acurácia quando o número de classes é baixo. Observou-se também que, ao considerar as configurações padrão dos métodos adotados, a abordagem espectral usualmente superou os demais algoritmos de agrupamento. Pela abordagem de redes complexas, esta tese mostra que, se considerarmos outro tipo de topologia de rede, além do modelo regular geralmente utilizado, haverá um impacto na acurácia da rede. Esta tese mostra que o impacto na acurácia é geralmente observado em escalas de tempo de aprendizado curto e médio. Esse comportamento foi observado usando três conjuntos de dados diferentes. Além disso, esta tese mostra como diferentes topologias também afetam a auto-organização do mapa topográfico da rede SOM. A auto-organização da rede foi estudada por meio do particionamento do mapa em grupos ou comunidades. Foram utilizadas quatro medidas topológicas para quantificar a estrutura dos grupos em três modelos distintos de rede: modularidade, número de elementos por grupo, número de grupos por mapa, tamanho do maior grupo. Em redes de pequeno mundo, os grupos se tornam mais densos à medida que o tempo aumenta. Um comportamento oposto a isso é encontrado nas redes assortativas. Apesar da modularidade, tem um alto valor em ambos os casos.
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Sedláček, Josef. "Algoritmy pro shlukování textových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218899.
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The thesis deals with text mining. It describes the theory of text document clustering as well as algorithms used for clustering. This theory serves as a basis for developing an application for clustering text data. The application is developed in Java programming language and contains three methods used for clustering. The user can choose which method will be used for clustering the collection of documents. The implemented methods are K medoids, BiSec K medoids, and SOM (self-organization maps). The application also includes a validation set, which was specially created for the diploma thesis and it is used for testing the algorithms. Finally, the algorithms are compared according to obtained results.
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Han, Yu. "Intelligent partial discharge diagnosis using SOM for turbogenerator condition monitoring." Thesis, Brunel University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249950.
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Malondkar, Ameya Mohan. "Extending the Growing Hierarchical Self Organizing Maps for a Large Mixed-Attribute Dataset Using Spark MapReduce." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33385.
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In this thesis work, we propose a Map-Reduce variant of the Growing Hierarchical Self Organizing Map (GHSOM) called MR-GHSOM, which is capable of handling mixed attribute datasets of massive size. The Self Organizing Map (SOM) has proved to be a useful unsupervised data analysis algorithm. It projects a high dimensional data onto a lower dimensional grid of neurons. However, the SOM has some limitations owing to its static structure and the incapability to mirror the hierarchical relations in the data. The GHSOM overcomes these shortcomings of the SOM by providing a dynamic structure that adapts its shape according to the input data. It is capable of growing dynamically in terms of the size of the individual neuron layers to represent data at the desired granularity as well as in depth to model the hierarchical relations in the data.
However, the training of the GHSOM requires multiple passes over an input dataset. This makes it difficult to use the GHSOM for massive datasets. In this thesis work, we propose a Map-Reduce variant of the GHSOM called MR-GHSOM, which is capable of processing massive datasets. The MR-GHSOM is implemented using the Apache Spark cluster computing engine and leverages the popular Map-Reduce programming model. This enables us to exploit the usefulness and dynamic capabilities of the GHSOM even for a large dataset.
Moreover, the conventional GHSOM algorithm can handle datasets with numeric attributes only. This is owing to the fact that it relies heavily on the Euclidean space dissimilarity measures of the attribute vectors. The MR-GHSOM further extends the GHSOM to handle mixed attribute - numeric and categorical - datasets. It accomplishes this by adopting the distance hierarchy approach of managing mixed attribute datasets.
The proposed MR-GHSOM is thus capable of handling massive datasets containing mixed attributes. To demonstrate the effectiveness of the MR-GHSOM in terms of clustering of mixed attribute datasets, we present the results produced by the MR-GHSOM on some popular datasets. We further train our MR-GHSOM on a Census dataset containing mixed attributes and provide an analysis of the results.
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Aksoy, Ece. "An Attempt To Classify Turkish District Data: K-means And Self-organizing Map (som) Algorithms." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605711/index.pdf.
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ABSTRACT
AN ATTEMPT TO CLASSIFY TURKISH DISTRICT DATA: K-MEANS AND SELF-ORGANIZING MAP (SOM) ALGORITHMS
Aksoy, Ece
M.S., Department of Geodetic and Geographic Information Systems
Supervisor: Assoc. Prof. Dr. Oguz ISik
December 2004, 112 pages
There is no universally applicable clustering technique in discovering the variety of structures display in data sets. Also, a single algorithm or approach is not adequate to solve every clustering problem. There are many methods available, the criteria used differ and hence different classifications may be obtained for the same data. While larger and larger amounts of data are collected and stored in databases, there is increasing the need for efficient and effective analysis methods. Grouping or classification of measurements is the key element in these data analysis procedures. There are lots of non-spatial clustering techniques in various areas. However, spatial clustering techniques and software are not so common.
This thesis is an attempt to classify Turkish district data with the help of two clustering algorithms: K-means clustering and self organizing maps (SOM). With the help of these two common techniques it is expected that a clustering can be reached, which can be used for different aims such as regional politics, constructing statistical integrity or analyzing distribution of funds, for same data in GIS environment and putting forward the facilitative usage of GIS in regional and statistical studies.
All districts of Turkey, which is 923 units, were chosen as an application area in this thesis. Some limitations such as population were specified for clustering of Turkey&rsquo<br>s districts. Firstly, different clustering techniques for spatial classification were researched. K-Means and SOM algorithms were chosen to compare different methods with Turkey&rsquo<br>s district data. Afterward, database of Turkey&rsquo<br>s statistical datum was formed and analyzed joining with geographical data in the GIS environment. Different clustering software, ArcGIS, CrimeStat and Matlab, were applied according to conclusion of clustering techniques research. Self Organizing Maps (SOM) algorithm, which is the best and most common spatial clustering algorithm in recent years, and CrimeStat K-Means clustering were used in this thesis as clustering methods.
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Thampi, Geetha K. "Identification and control of nonlinear systems using multiple models based on the self-organizing map (SOM)." [Gainesville, Fla.]: University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0000804.
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Stefanovič, Pavel. "Saviorganizuojančių neuroninių tinklų (SOM) sistemų lyginamoji analizė." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20100709_133927-01419.
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Šiame darbe pateikti ir aprašyti biologinio ir dirbtinio neurono modeliai. Didžiausias dėmesys
skiriamas vieno tipo neuroniniams tinklams – saviorganizuojantiems žemėlapiams (SOM). Darbe
pateiktas jų apmokymas, taip pat pagrindinių sąvokų (epocha, kaimynystės eilė, unifikuotų atstumų
matrica ir kt.), susijusių su SOM neuroniniais tinklais (žemėlapiais), apibrėžimai. Buvo nagrinėtos
keturios saviorganizuojančių neuroninių tinklų sistemos: NeNet, SOM-Toolbox, DataBionic ESOM,
Viscovery SOMine ir Matlab įrankiai „nntool“, „nctool“, kurie naudojami SOM tinklams sukurti ir
apmokyti. Pateikiamos sistemų naudojimosi instrukcijos, norint gauti paprasčiausią SOM žemėlapį.
Matlab aplinkoje sukurta ir darbe aprašyta naują vizualizavimo būdą turinti sistema „Somas“, pateiktas
jos išskirtinumas ir naudojimosi instrukcija. Sistemoje „Somas“ realizuota kita mokymo funkcija nei
kitose minėtose sistemose. Pagrindinis analizuotų sistemų tikslas yra suskirstyti duomenis į klasterius
pagal jų panašumą ir pateikti juos SOM žemėlapyje. Sistemos viena nuo kitos skiriasi duomenų
pateikimu, mokymo taisyklėmis, vizualizavimo galimybėmis, todėl čia aptariami sistemų panašumai ir
skirtumai. Nagrinėti susidarę SOM žemėlapiai ir gautos kvantavimo bei topografinės paklaidos,
analizuojant tris duomenų aibes: irisų, stiklo ir vyno. Kvantavimo ir topografinės paklaidos yra
kiekybiniai vaizdo kokybės įverčiai. Padarytos išvados apie susidariusius klasterius tiriamuose
duomenyse. Naudojant naują sistemą „Somas“... [toliau žr. visą tekstą]<br>In this master thesis, biologic and artificial neuron models have been described. The focus is selforganizing
maps (SOM). The self-organizing maps are one of types of artificial neural networks. SOM
training as well as the main concepts which need to explain SOM networks (epochs, neighbourhood
size, u-matrix and etc.) have been described. Four systems of self-organizing maps: NeNet, SOMToolbox,
DataBionic ESOM, Viscovery SOMine, and Matlab tools “nntool” and “nctool” have been
analyzed. In the thesis, a system use guide has been presented to make a simple SOM map. A new
system “Somas” that has a new visualisation way has been developed in Matlab. The system has been
described, its oneness has been emphasized, and a use guide is presented. The main target of the SOM
systems is data clustering and their graphical presentation on the self-organizing map. The SOM
systems are different one from other in their interfaces, the data pre-processing, learning rules,
visualization manners, etc. Similarities and differences of the systems have been highlighted here. The
experiments have been carried out with three data sets: iris, glass and wine. The SOM maps, obtained
by each system, have been described and some conclusions on the clusters have been drawn. The
quantization and topographic errors have been analyzed to estimate the quality of the maps obtained.
An investigation has been carried out in the new system “Somas” and system “NeNet” in order to look
how quantization and... [to full text]
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Štrimaitis, Rokas. "Saviorganizuojančio neuroninio tinklo (SOM) ir jo modifikacijos daugiamačiams duomenims vizualizuoti (ViSOM) lyginamoji analizė." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20100712_093304-05293.
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Saviorganizuojantys neuroniniai tinklai (SOM) yra susilaukę nemažai populiarumo mokslininkų tarpe klasterizuojant ar vizualizuojant daugiamačius duomenis. Šiame magistro diplominiame darbe smulkiai išnagrinėtas SOM algoritmas bei veikimo principai, pateiktos galimos parametrų reikšmės ir kaimynystės funkcijos. Taip pat nurodyti tinklo kokybės įvertinimo kriterijai ir duomenų vizualizavimo metodai taikant saviorganizuojantį neuroninį tinklą. SOM pagrindinis tikslas yra duomenų klasterizavimas, o ne vizualizavimas, todėl duomenų vaizdavimas SOM'u turi savų trūkumų – žemėlapyje negalima matyti atstumų tarp klasės neuronų ir kaip toli nutolusios viena nuo kitos klasės. Pateikta alternatyva – SOM modifikacija ViSOM. Darbe išnagrinėti ViSOM algoritmo esminiai skirtumai, aprašyti parametrų parinkimo ypatumai. Nagrinėti SOM ir ViSOM vizualizavimo skirtumus sukurta MATLAB sistemoje programa, realizuojanti abu algoritmus bei pateiktas programos galimybių ir scenarijų aprašas. Pasirinkus tris kaimynines funkcijas su šia programa atlikti tyrimai, rodantys, kad kvantavimo ir topografinės paklaidos netinkamos vertinant ViSOM vaizdo kokybę. Pasiūlyti trys nauji vertinimo kriterijai, bei su jais atlikti tyrimai, parodantys jų veiksmingumą. Taip pat darbe vizualiai parodytas ir aprašytas ViSOM žemėlapio kitimas priklausomai nuo rezoliucijos.<br>A self-organizing map is a type of artificial neural networks that has received substantial popularity among scientists in regards to clustering and visualization of multidimensional data. In this master theses, the learning algorithm and the main principals are examined in detail, the neighbourhood functions and values of various parameters are given. Some criteria of the network evaluation quality and the data visualization methods using the self-organizing maps are given as well.
The main goal of the SOM is clustering of data, but not the visualization, so the visual data representation by the SOM has its drawbacks – it is impossible to see the distances between neurons, corresponding the vectors belong to a class, and how far from each other the classes are in a map. The alternative – SOM modification, called ViSOM, has been developed. The main differences of SOM and ViSOM are investigated, the peculiarity of parameter selection is also examined in this work.
In order to study the differences of SOM and ViSOM visualization, a system in MATLAB has been developed, both algorithms have been implemented, and the feature and scenario list of the program is presented. Some experiments have been carried out by selecting three neighborhood functions. The experiments have showed that the quantization and topographic errors are not suitable for studying the visualization of ViSOM. Three new evaluation criteria are proposed. The investigation shows their effectiveness. In the work... [to full text]
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Langin, Chester Louis. "A SOM+ Diagnostic System for Network Intrusion Detection." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/dissertations/389.
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This research created a new theoretical Soft Computing (SC) hybridized network intrusion detection diagnostic system including complex hybridization of a 3D full color Self-Organizing Map (SOM), Artificial Immune System Danger Theory (AISDT), and a Fuzzy Inference System (FIS). This SOM+ diagnostic archetype includes newly defined intrusion types to facilitate diagnostic analysis, a descriptive computational model, and an Invisible Mobile Network Bridge (IMNB) to collect data, while maintaining compatibility with traditional packet analysis. This system is modular, multitaskable, scalable, intuitive, adaptable to quickly changing scenarios, and uses relatively few resources.
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Gharani, Pedram. "Modeling spatial accessibility for in-vitro fertility (IVF) care services in Iowa." Thesis, University of Iowa, 2014. https://ir.uiowa.edu/etd/1459.
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Vadoodi, Roshanak. "Geophysical vectoring of mineralized systems in northern Norrbotten." Licentiate thesis, Luleå tekniska universitet, Geovetenskap och miljöteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-82408.
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The Fennoscandian Shield as a part of a large Precambrian basement area is located in northern Europe and hosts economically important mineral deposits including base metals and precious metals. Regional geophysical data such as potential field and magnetotelluric data in combination with other geoscientific data contain information of importance for an understanding of the crustal and upper mantle structure. Knowledge about regional-scale structures is important for an optimized search for mineralisation. In order to investigate in more detail the spatial distribution of regional electrically conductive structures and near-surface mineral deposits, complementary magnetotelluric measurements have been done within the Precambrian Shield in the north-eastern part of the Norrbotten ore province. The potential field data provided by the Geological Survey of Sweden have been included in the current study. Processing of magnetotelluric data was performed using a robust multi-remote reference technique. The dimensionality analysis of the phase tensors indicates complex 3D structures in the area. A 3D crustal model of the electrical conductivity structure was derived based on 3D inversion of the data using the ModEM code. The final inversion 3D resistivity model revealed the presence of strong crustal conductors with the conductance of more than 3000 S at depth of tens of kilometres within a generally resistive crust. A significant part of the middle crust conductors is elongated in directions that coincide with major ductile deformation zones that have been mapped from airborne magnetic data and geological fieldwork. Some of these conductors have near-surface expression where they spatially correlate with the locations of known mineralisation. Processing and 3D inversion of the regional magnetic and gravity field data were performed, and the structural information derived from these data by using an open-source object-oriented package code written in Python called SimPEG. In this study, a new approach is proposed to extract and analyse the correlation between the modelled physical properties and for domain classification. For this, a neural net Self-Organizing Map procedure (SOM) was used for data reduction and simplification. The input data to the SOM analysis contain resistivity, magnetic susceptibility, and density model values for some selected depth levels. The domain classification is discussed with respect to geological boundaries and composition. The classification is furthermore applied for prediction of favourable areas for mineralisation. Based on visual inspection of processed regional gravity and magnetic field data and a SOM analysis performed on higher-order derivatives of the magnetic data, an interpretation of a sinistral fault with 52 km offset is proposed. The fault is oriented N10E and can be traced 250 km from Karesuando at the Swedish-Finish border southwards to the Archaean-Proterozoic boundary marked by the Luleå-Jokkmokk Zone.
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Leloup, Julie. "Influence du changement climatique sur les caractéristiques d'ENSO par méthodes neuronales." Paris 6, 2006. http://www.theses.fr/2006PA066618.
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Le phénomène El Niño-Southern Oscillation est le mode de variabilité climatique le plus énergétique aux échelles interannuelles. Quelles sont les caractéristiques de cette variabilité et plus particulièrement des événements El Niño/La Niña et comment sont-elles susceptibles d'évoluer en réponse au changement climatique ? À l'aide d'une méthodologie originale, reposant sur l'utilisation des cartes de Kohonen (SOM, Self-Organizing Map) et la température de surface de l'océan Pacifique équatorial, certaines modifications d'ENSO sont identifiées au cours de la période 1950-2002 et la méthode est validée. Dans un second temps, la base de données IPCC (Intergovernmental Panel on Climate Change) est explorée à l'aide de SOM. La variabilité ENSO simulée par les modèles forcés par les conditions climatiques du XXe siècle est validée par les observations. Les événements El Niño/La Niña sont étudiés distinctement et des biais sont mis en évidence. Enfin, un scénario futur est analysé pour proposer des évolutions des caractéristiques identifiées au XXe siècle. Il est montré qu'il n'y a pas de consensus quant à leur devenir<br>El Niño/Southern Oscillation (ENSO) is the strongest climate perturbation on interannual time scales. What are ENSO characteristics and more specifically El Niño/La Niña events characteristics, and how will they evoluate in response to climate change? Using an original methodology, based on the use of Kohonen maps (SOM, Self-Organizing Maps) and the equatorial Pacific sea surface temperatures, modifications in ENSO are identified during the 1950-2002 period and the method is validated. Then the IPCC (Intergovernmental Panel on Climate Change) is investigated using SOM. The ENSO variability as simulated by models forced by the twentieth century conditions is validated with observations. In particular, El Niño and La Niña events are studied and biases are highlighted. Finally futur scenario is analyzed to propose evolution for those characteristics. Results do not show any consensus
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Jansson, Mattias, and Jimmy Johansson. "Interactive Visualization of Statistical Data using Multidimensional Scaling Techniques." Thesis, Linköping University, Department of Science and Technology, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1716.
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<p>This study has been carried out in cooperation with Unilever and partly with the EC founded project, Smartdoc IST-2000-28137. </p><p>In areas of statistics and image processing, both the amount of data and the dimensions are increasing rapidly and an interactive visualization tool that lets the user perform real-time analysis can save valuable time. Real-time cropping and drill-down considerably facilitate the analysis process and yield more accurate decisions. </p><p>In the Smartdoc project, there has been a request for a component used for smart filtering in multidimensional data sets. As the Smartdoc project aims to develop smart, interactive components to be used on low-end systems, the implementation of the self-organizing map algorithm proposes which dimensions to visualize. </p><p>Together with Dr. Robert Treloar at Unilever, the SOM Visualizer - an application for interactive visualization and analysis of multidimensional data - has been developed. The analytical part of the application is based on Kohonen’s self-organizing map algorithm. In cooperation with the Smartdoc project, a component has been developed that is used for smart filtering in multidimensional data sets. Microsoft Visual Basic and components from the graphics library AVS OpenViz are used as development tools.</p>
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Thögersen, Josefina, and Sandra Gimestam-Jarl. "Att skada sig med sex : En kvalitativ studie om professionellas syn på och arbete med unga som har ett destruktivt sexuellt beteende." Thesis, Stockholms universitet, Institutionen för socialt arbete - Socialhögskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-77477.
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The aim of this study is to shed light on the phenomenon of destructive sexual behavior among youth from the perspective of professionals working at nonprofit organizations. To examine this, we used a qualitative method; we interviewed professionals working at nonprofit organizations. To analyze our results we used gender theory and empowerment theory. Our main results are that the professionals view this behavior as deliberate self-harm, such as cutting, as they do it to achieve relief from anxiety. Also, the professionals think that gender norms affect young people with destructive sexual behavior in that it imposes guilt and shame, which inflicts their already low self-esteem and makes them feel inferior. The profes- sionals view this as the common denominator for this group of young people. Therefore it is very important for professionals to help these young people build self-esteem by using empowerment. Hopefully, this study can provide additional knowledge to the field of social work, due to the focus on a relatively new perspective on the phenomenon of deliberate self-harm. It is therefore important for agents in social work to have knowledge and comprehension about how to approach the phenomena and how to view and work with this group of young people.
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Štrba, Miroslav. "Rozpoznání ručně psaných číslic." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237266.
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Recognition of handwritten digits is a problem, which could serve as model task for multiclass recognition of image patterns. This thesis studies different kinds of algoritms (Self-Organizing Maps, Randomized tree and AdaBoost) and methods for increasing accuracy using fusion (majority voting, averaging log likelihood ratio, linear logistic regression). Fusion methods were used for combine classifiers with indentical train parameters, with different training methods and with multiscale input.
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Abdallah, Abdallah Sabry. "Investigation of New Techniques for Face detection." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/33191.
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The task of detecting human faces within either a still image or a video frame is one of the most popular object detection problems. For the last twenty years researchers have shown great interest in this problem because it is an essential pre-processing stage for computing systems that process human faces as input data. Example applications include face recognition systems, vision systems for autonomous robots, human computer interaction systems (HCI), surveillance systems, biometric based authentication systems, video transmission and video compression systems, and content based image retrieval systems.
In this thesis, non-traditional methods are investigated for detecting human faces within color images or video frames. The attempted methods are chosen such that the required computing power and memory consumption are adequate for real-time hardware implementation. First, a standard color image database is introduced in order to accomplish fair evaluation and benchmarking of face detection and skin segmentation approaches. Next, a new pre-processing scheme based on skin segmentation is presented to prepare the input image for feature extraction. The presented pre-processing scheme requires relatively low computing power and memory needs. Then, several feature extraction techniques are evaluated. This thesis introduces feature extraction based on Two Dimensional Discrete Cosine Transform (2D-DCT), Two Dimensional Discrete Wavelet Transform (2D-DWT), geometrical moment invariants, and edge detection. It also attempts to construct a hybrid feature vector by the fusion between 2D-DCT coefficients and edge information, as well as the fusion between 2D-DWT coefficients and geometrical moments. A self organizing map (SOM) based classifier is used within all the experiments to distinguish between facial and non-facial samples. Two strategies are tried to make the final decision from the output of a single SOM or multiple SOM. Finally, an FPGA based framework that implements the presented techniques, is presented as well as a partial implementation.
Every presented technique has been evaluated consistently using the same dataset. The experiments show very promising results. The highest detection rate of 89.2% was obtained when using a fusion between DCT coefficients and edge information to construct the feature vector. A second highest rate of 88.7% was achieved by using a fusion between DWT coefficients and geometrical moments. Finally, a third highest rate of 85.2% was obtained by calculating the moments of edges.<br>Master of Science
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Yogeswaran, Arjun. "Self-Organizing Neural Visual Models to Learn Feature Detectors and Motion Tracking Behaviour by Exposure to Real-World Data." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37096.
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Advances in unsupervised learning and deep neural networks have led to increased performance in a number of domains, and to the ability to draw strong comparisons between the biological method of self-organization conducted by the brain and computational mechanisms. This thesis aims to use real-world data to tackle two areas in the domain of computer vision which have biological equivalents: feature detection and motion tracking.
The aforementioned advances have allowed efficient learning of feature representations directly from large sets of unlabeled data instead of using traditional handcrafted features. The first part of this thesis evaluates such representations by comparing regularization and preprocessing methods which incorporate local neighbouring information during training on a single-layer neural network. The networks are trained and tested on the Hollywood2 video dataset, as well as the static CIFAR-10, STL-10, COIL-100, and MNIST image datasets. The induction of topography or simple image blurring via Gaussian filters during training produces better discriminative features as evidenced by the consistent and notable increase in classification results that they produce. In the visual domain, invariant features are desirable such that objects can be classified despite transformations. It is found that most of the compared methods produce more invariant features, however, classification accuracy does not correlate to invariance.
The second, and paramount, contribution of this thesis is a biologically-inspired model to explain the emergence of motion tracking behaviour in early development using unsupervised learning. The model’s self-organization is biased by an original concept called retinal constancy, which measures how similar visual contents are between successive frames. In the proposed two-layer deep network, when exposed to real-world video, the first layer learns to encode visual motion, and the second layer learns to relate that motion to gaze movements, which it perceives and creates through bi-directional nodes. This is unique because it uses general machine learning algorithms, and their inherent generative properties, to learn from real-world data. It also implements a biological theory and learns in a fully unsupervised manner. An analysis of its parameters and limitations is conducted, and its tracking performance is evaluated. Results show that this model is able to successfully follow targets in real-world video, despite being trained without supervision on real-world video.
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Ozcan, Yavuzoglu Seyma. "An Evaluation Of Clustering And Districting Models For Household Socio-economic Indicators In Address-based Population Register System." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611471/index.pdf.
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Census operations are very important events in the history of a nation. These operations cover every bit of land and property of the country and its citizens. Census data is also known as demographic data providing valuable information to various users, particularly planners to know the trends in the key areas. Since 2006, Turkey aims to produce this census data not as &ldquo<br>de-facto&rdquo<br>(static) but as &ldquo<br>de-jure&rdquo<br>(real-time) by the new Address Based Register Information System (ABPRS). Besides, by this new register based census, personal information is matched with their address information and censuses gained a spatial dimension. Data obtained from this kind of a system can be a great input for the creation of &ldquo<br>small statistical areas (SSAs)&rdquo<br>which can compose of street blocks or any other small geographical unit to which social data can be referenced and to establish a complete census geography for Turkey. Because, statistics on large administrative units are only necessary for policy design only at an extremely abstracted level of analysis which is far from "<br>real"<br>problems as experienced by individuals.
In this thesis, it is aimed to employ some spatial clustering and districting methodologies to automatically produce SSAs which are basically built upon the ABPRS data that is geo-referenced with the aid of geographical information systems (GIS) and thus help improving the census geography concept which is limited with only higher level administrative boundaries in Turkey. In order to have a clear idea of what strategy to choose for its realization, small area identification criteria and methodologies are searched by looking into the United Nations&rsquo<br>recommendations and by taking some national and international applications into consideration. In addition, spatial clustering methods are examined for obtaining SSAs which fulfills these criteria in an automated fashion. Simulated annealing on k-means clustering, only k-means clustering and simulated annealing on k-means clustering of Self-Organizing Map (SOM) unified distances are deemed as suitable methods. Then these methods are implemented on parcel and block datasets having either raw data or socio-economic status (SES) indices in nine neighborhoods of Keç<br>iö<br>ren whose graphical and non-graphical raw data are manipulated, geo-referenced and combined in common basemaps. Consequently, simulated annealing refinement on k-means clustering of SOM u-distances is selected as the optimum method for constructing SSAs for all datasets after making a comparative quality assessment study which allows us to see how much each method obeyed the basic criteria of small area identification while creating SSA layers.
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Nordqvist, My. "Classify part of day and snow on the load of timber stacks : A comparative study between partitional clustering and competitive learning." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42238.
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In today's society, companies are trying to find ways to utilize all the data they have, which considers valuable information and insights to make better decisions. This includes data used to keeping track of timber that flows between forest and industry. The growth of Artificial Intelligence (AI) and Machine Learning (ML) has enabled the development of ML modes to automate the measurements of timber on timber trucks, based on images. However, to improve the results there is a need to be able to get information from unlabeled images in order to decide weather and lighting conditions. The objective of this study is to perform an extensive for classifying unlabeled images in the categories, daylight, darkness, and snow on the load. A comparative study between partitional clustering and competitive learning is conducted to investigate which method gives the best results in terms of different clustering performance metrics. It also examines how dimensionality reduction affects the outcome. The algorithms K-means and Kohonen Self-Organizing Map (SOM) are selected for the clustering. Each model is investigated according to the number of clusters, size of dataset, clustering time, clustering performance, and manual samples from each cluster. The results indicate a noticeable clustering performance discrepancy between the algorithms concerning the number of clusters, dataset size, and manual samples. The use of dimensionality reduction led to shorter clustering time but slightly worse clustering performance. The evaluation results further show that the clustering time of Kohonen SOM is significantly higher than that of K-means.
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Skřížala, Martin. "Využití neuronových sítí v klasifikaci srdečních onemocnění." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217210.
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This thesis discusses the design and the utilization of the artificial neural networks as ECG classifiers and the detectors of heart diseases in ECG signal especially myocardial ischaemia. The changes of ST-T complexes are the important indicator of ischaemia in ECG signal. Different types of ischaemia are expressed particularly by depression or elevation of ST segments and changes of T wave. The first part of this thesis is orientated towards the theoretical knowledges and describes changes in the ECG signal rising close to different types of ischaemia. The second part deals with to the ECG signal pre-processing for the classification by neural network, filtration, QRS detection, ST-T detection, principal component analysis. In the last part there is described design of detector of myocardial ischaemia based on artificial neural networks with utilisation of two types of neural networks back – propagation and self-organizing map and the results of used algorithms. The appendix contains detailed description of each neural networks, description of the programme for classification of ECG signals by ANN and description of functions of programme. The programme was developed in Matlab R2007b.
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Rodrigues, Fabiene Silva. "Métodos de agrupamento na análise de dados de expressão gênica." Universidade Federal de São Carlos, 2009. https://repositorio.ufscar.br/handle/ufscar/4537.
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Previous issue date: 2009-02-16<br>The clustering techniques have frequently been used in literature to the analyse data in several fields of application. The main objective of this work is to study such techniques. There is a large number of clustering techniques in literature. In this work we concentrate on Self Organizing Map (SOM), k-means, k-medoids and Expectation- Maximization (EM) algorithms. These algorithms are applied to gene expression data. The analisys of gene expression, among other possibilities, identifies which genes are differently expressed in synthesis of proteins associated to normal and sick tissues. The purpose is to do a comparing of these metods, sticking out advantages and disadvantages of such. The metods were tested for simulation and after we apply them to a real data set.<br>As técnicas de agrupamento (clustering) vêm sendo utilizadas com freqüência na literatura para a solução de vários problemas de aplicações práticas em diversas áreas do conhecimento. O principal objetivo deste trabalho é estudar tais técnicas. Mais especificamente, estudamos os algoritmos Self Organizing Map (SOM), k-means, k-medoids, Expectation-Maximization (EM). Estes algoritmos foram aplicados a dados de expressão gênica. A análise de expressão gênica visa, entre outras possibilidades, a identificação de quais genes estão diferentemente expressos na sintetização de proteínas associados a tecidos normais e doentes. O objetivo deste trabalho é comparar estes métodos no que se refere à eficiência dos mesmos na identificação de grupos de elementos similares, ressaltando vantagens e desvantagens de cada um. Os métodos foram testados por simulação e depois aplicamos as metodologias a um conjunto de dados reais.
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Sutharzan, Sreeskandarajan. "CLUSTERING AND VISUALIZATION OF GENOMIC DATA." Miami University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=miami1563973517163859.
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Muharish, Essa Yahya M. "PACKET FILTER APPROACH TO DETECT DENIAL OF SERVICE ATTACKS." CSUSB ScholarWorks, 2016. https://scholarworks.lib.csusb.edu/etd/342.
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Denial of service attacks (DoS) are a common threat to many online services. These attacks aim to overcome the availability of an online service with massive traffic from multiple sources. By spoofing legitimate users, an attacker floods a target system with a high quantity of packets or connections to crash its network resources, bandwidth, equipment, or servers. Packet filtering methods are the most known way to prevent these attacks via identifying and blocking the spoofed attack from reaching its target. In this project, the extent of the DoS attacks problem and attempts to prevent it are explored. The attacks categories and existing countermeasures based on preventing, detecting, and responding are reviewed. Henceforward, a neural network learning algorithms and statistical analysis are utilized into the designing of our proposed packet filtering system.
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Simán, Frans Filip. "Assessment of Machine Learning Applied to X-Ray Fluorescence Core Scan Data from the Zinkgruvan Zn-Pb-Ag Deposit, Bergslagen, Sweden." Thesis, Luleå tekniska universitet, Geovetenskap och miljöteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-82050.
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Lithological core logging is a subjective and time consuming endeavour which could possibly be automated, the question is if and to what extent this automation would affect the resulting core logs. This study presents a case from the Zinkgruvan Zn-Pb-Ag mine, Bergslagen, Sweden; in which Classification and Regression Trees and K-means Clustering on the Self Organising Map were applied to X-Ray Flourescence lithogeochemistry data derived from automated core scan technology. These two methods are assessed through comparison to manual core logging. It is found that the X-Ray Fluorescence data are not sufficiently accurate or precise for the purpose of automated full lithological classification since not all elements are successfully quantified. Furthermore, not all lithologies are possible to distinquish with lithogeochemsitry alone furter hindering the success of automated lithological classification. This study concludes that; 1) K-means on the Self Organising Map is the most successful approach, however; this may be influenced by the method of domain validation, 2) the choice of ground truth for learning is important for both supervised learning and the assessment of machine learning accuracy and 3) geology, data resolution and choice of elements are important parameters for machine learning. Both the supervised method of Classification and Regression Trees and the unsupervised method of K-means clustering applied to Self Organising Maps show potential to assist core logging procedures.
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Jamborová, Soňa. "Segmentace obrazu pomocí neuronové sítě." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-236925.
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This work is about suggestion of the software for neural network based image segmentation. It defines basic terms for this topics. It is focusing mainly at preperation imaging information for image segmentation using neural network. It describes and compares different aproaches for image segmentation.
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Martínez, Brito Izacar Jesús. "Quantitative structure fate relationships for multimedia environmental analysis." Doctoral thesis, Universitat Rovira i Virgili, 2010. http://hdl.handle.net/10803/8590.
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Key physicochemical properties for a wide spectrum of chemical pollutants are unknown. This thesis analyses the prospect of assessing the environmental distribution of chemicals directly from supervised learning algorithms using molecular descriptors, rather than from multimedia environmental models (MEMs) using several physicochemical properties estimated from QSARs. Dimensionless compartmental mass ratios of 468 validation chemicals were compared, in logarithmic units, between: a) SimpleBox 3, a Level III MEM, propagating random property values within statistical distributions of widely recommended QSARs; and, b) Support Vector Regressions (SVRs), acting as Quantitative Structure-Fate Relationships (QSFRs), linking mass ratios to molecular weight and constituent counts (atoms, bonds, functional groups and rings) for training chemicals. Best predictions were obtained for test and validation chemicals optimally found to be within the domain of applicability of the QSFRs, evidenced by low MAE and high q2 values (in air, MAE≤0.54 and q2≥0.92; in water, MAE≤0.27 and q2≥0.92).<br>Las propiedades fisicoquímicas de un gran espectro de contaminantes químicos son desconocidas. Esta tesis analiza la posibilidad de evaluar la distribución ambiental de compuestos utilizando algoritmos de aprendizaje supervisados alimentados con descriptores moleculares, en vez de modelos ambientales multimedia alimentados con propiedades estimadas por QSARs. Se han comparado fracciones másicas adimensionales, en unidades logarítmicas, de 468 compuestos entre: a) SimpleBox 3, un modelo de nivel III, propagando valores aleatorios de propiedades dentro de distribuciones estadísticas de QSARs recomendados; y, b) regresiones de vectores soporte (SVRs) actuando como relaciones cuantitativas de estructura y destino (QSFRs), relacionando fracciones másicas con pesos moleculares y cuentas de constituyentes (átomos, enlaces, grupos funcionales y anillos) para compuestos de entrenamiento. Las mejores predicciones resultaron para compuestos de test y validación correctamente localizados dentro del dominio de aplicabilidad de los QSFRs, evidenciado por valores bajos de MAE y valores altos de q2 (en aire, MAE≤0.54 y q2≥0.92; en agua, MAE≤0.27 y q2≥0.92).
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Janse, Van Vuuren Michaella. "Human Pose and Action Recognition using Negative Space Analysis." Diss., University of Cape Town, 2004. http://hdl.handle.net/10919/71571.
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This thesis proposes a novel approach to extracting pose information from image sequences. Current state of the art techniques focus exclusively on the image space occupied by the body for pose and action recognition. The method proposed here, however, focuses on the negative spaces: the areas surrounding the individual. This has resulted in the colour-coded negative space approach, an image preprocessing step that circumvents the need for complicated model fitting or template matching methods. The approach can be described as follows: negative spaces surrounding the human silhouette are extracted using horizontal and vertical scanning processes. These negative space areas are more numerous, and undergo more radical changes in shape than the single area occupied by the figure of the person performing an action. The colour-coded negative space representation is formed using the four binary images produced by the scanning processes. Features are then extracted from the colour-coded images. These are based on the percentage of area occupied by distinct coloured regions as well as the bounding box proportions. Pose clusters are identified using feedback from an independent action set. Subsequent images are classified using a simple Euclidean distance measure. An image sequence is thus temporally segmented into its corresponding pose representations. Action recognition simply becomes the detection of a temporally ordered sequence of poses that characterises the action. The method is purely vision-based, utilising monocular images with no need for body markers or special clothing. Two datasets were constructed using several actors performing different poses and actions. Some of these actions included actors waving their arms, sitting down or kicking a leg. These actions were recorded against a monochrome background to simplify the segmentation of the actors from the background. The actions were then recorded on DV cam and digitised into a data base. The silhouette images from these actions were isolated and placed in a frame or bounding box. The next step was to highlight the negative spaces using a directional scanning method. This scanning method colour-codes the negative spaces of each action. What became immediately apparent is that very distinctive colour patterns formed for different actions. To emphasise the action, different colours were allocated to negative spaces surrounding the image. For example, the space between the legs of an actor standing in a T - pose with legs apart would be allocated yellow, while the space below the arms were allocated different shades of green. The space surrounding the head would be different shades of purple. During an action when the actor moves one leg up in a kicking fashion, the yellow colour would increase. Inversely, when the actor closes his legs and puts them together, the yellow colour filling the negative space would decrease substantially. What also became apparent is that these coloured negative spaces are interdependent and that they influence each other during the course of an action. For example, when an actor lifts one of his legs, increasing the yellow-coded negative space, the green space between that leg and the arm decreases. This interrelationship between colours hold true for all poses and actions as presented in this thesis. In terms of pose recognition, it is significant that these colour coded negative spaces and the way the change during an action or a movement are substantial and instantly recognisable. Compare for example, looking at someone lifting an arm as opposed to seeing a vast negative space changing shape. In a controlled research environment, several actors were instructed to perform a number of different actions. After colour coding the negative spaces, it became apparent that every action can be recognised by a unique colour coded pattern. The challenge is to ascribe a numerical presentation, a mathematical quotation, to extract the essence of what is so visually apparent. The essence of pose recognition and it's measurability lies in the relationship between the colours in these negative spaces and how they impact on each other during a pose or an action. The simplest way of measuring this relationship is by calculating the percentage of each colour present during an action. These calculated percentages become the basis of pose and action recognition. By plotting these percentages on a graph confirms that the essence of these different actions and poses can in fact been captured and recognised. Despite variations in these traces caused by time differences, personal appearance and mannerisms, what emerged is a clear recognisable pattern that can be married to an action or different parts of an action. 7 Actors might lift their left leg, some slightly higher than others, some slower than others and these variations in terms of colour percentages would be recorded as a trace, but there would be very specific stages during the action where the traces would correspond, making the action recognisable.In conclusion, using negative space as a tool in human pose and tracking recognition presents an exiting research avenue because it is influenced less by variations such as difference in personal appearance and changes in the angle of observation. This approach is also simplistic and does not rely on complicated models and templates
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Lin, Wen-Der, and 林文得. "Using Self-Organization Map (SOM) in unsupervised cluster network performance problems study." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/56396560973787942155.
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碩士<br>樹德科技大學<br>資訊管理研究所<br>96<br>Self-Organization Maps (SOM) is an excellent data mining tools, not only used to interpret the high-dimensional mapping data in order to design the output of low-dimension topology but also provides users with visual data on the nature of the cluster. Researchers can study through the learning process by updating the input and output value of link weight as well as the data in future action. There are no uniform standard numerical patterns in the database field; moreover, most of data mining is applicable to single database field numerical patterns. Nowadays, scholars often use the advanced computer technology to approach cluster analysis for data without official supervision, yet the design parameters in the course of these clusters will obviously affect the convergence process. The purpose of this study is to investigate the Self-Organizing Maps network that needs the parameters of the database field; numerical patterns of different clusters affect the quality and efficiency effects. Furthermore, this study adds Taguchi’s quality design to explore the way a group of numerical data in different types of cluster analysis preceded to obtaining better quality and efficiency. The traditional K-Means statistical method in terms of cluster analysis as a cluster of relative error rate successively led to a group of the cluster performance superior to the traditional parameters of the cluster approach. From the experimental results, Self-Organizing Maps networks applied for the problem of better clusters performance successfully replace the K-Means statistical method for the cluster effect. The study of the experimental parameters setting selected by the application of the database can be used in future experiments in terms of the ideas and parameters set.
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WANG, KAI-PING, and 王開屏. "Digital radio communication using self-organization map." Thesis, 1991. http://ndltd.ncl.edu.tw/handle/61176871496909219583.
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Hsu, Yu-Yao, and 許譽耀. "A Visualized Deep Learning Based on Multilayer Self-Organization Map." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/v8y2fb.
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碩士<br>國立中央大學<br>資訊工程學系<br>106<br>In recent years, many people try to understand the structure of the brain. We believed that if we can add machines to learning. It is possible to use machines instead of humans and do some simple, repetitive things.
This neural network architecture is a combination of supervised and unsupervised neural networks. This paper uses the k-means algorithm to group the input image. And then use the self-organizing map to generate feature map. And then use adaptive resonance theory to save the results of response map. Finally, use the learning vector quantization network fine-tuning the results of adaptive resonance theory without the use of Gradient. In addition, we can visualize and transform each layer of features into images that can be understood by the human’s eyes.
In this paper’s experiments, we use two types of datasets. One is MNIST, another is song datasets. The experiment includes the feature maps, the alert parameters of the adaptive resonance theory, binary of image, and image combination. In the end, the feature is visualized presented
The experiment in this thesis has compared to the feature maps, the alert parameters of ART, the image binary, and the different method of the image’s merger.
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沈紋任. "Visualized Prediction Model of Financial Distress using Self Organization Map." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/43760229764304772522.
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碩士<br>國立交通大學<br>財務金融研究所<br>93<br>This study tries to construct a visualed early warning model of financial distress for the listed companies in Taiwan using the self organization map method. The result can help the investor find out the problem companies in the future, and warn the managers to modify the operational strategy. The first part of the study is to train the model using the financial ratios in the financial statements. The model’s early warning ability and how it is affected by the input data can be analyzed. The second part is to check the tracks of financial ratios of the distressed companies over a three year period. In this way we can really observe the financial performance of these distressed companies going from bad to worse in three years. Although much to be done, the results of this study show that the SOM model can achieve the purpose for visualized warning of problem companies in advance.
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Yi-Jen, Chiu, and 邱怡仁. "Motion estimation of Dynamic Image using Self-Organization Feature Map Method." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/34193942581098137956.
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碩士<br>中原大學<br>電機工程研究所<br>86<br>An accurate estimate of pixel displacement motion vectors in two consecutive moving images can help to provide the image deformation information to make the predicted moving image approaching to the true image. The estimated displacement vectors and the image residue error between motion estimated image, i.e. the deformation image, and true image are usually used in coding a series of motion images. Based on the motion estimation technique, all images can be compressed effectively. The system can also speeds up the transmission time for the purpose of the multi media image communication. In this thesis, an image motion estimation method based on the self-organization feature maps (SOFM) is presented. According to the changes of elastic deformity in the context of a series of motion image, the image motion information is estimated and presented by the displacement vectors. The first step of the approach procedures is to divide each image into a set of square size subimage. Then, connecting the feature value of each subimage to the input layer of the self-organizing neural nets.The corresponding feature of the target image would provide important information for the input neurons to adjust the relative position of each subimage. Through the adaptive matching process iteratively, the displacement vectors for the individual subimage are estimated and used to elastically deform the original image to the target image. The advantage of this method is that it combines the property of "network topology" and the spatial interconnection of "neighborhood". So that it has the ability to overcome the noise and restoration of the images in a self-organization manner. In order to compare the performance of the presented method with the other traditional motion estimation methods, We measured the final results of some motion images that come from simulated and real medical data. In general, this thesis uses the self-organization feature map method to estimation the motion vectors of the dynamic images and the correctness of the predicted direction and velocity of the moving object of the image is also verified. This method also can be applied to the precise dynamic medical images; in the repetitious experiments, the SOFM method is a better method to improve the quality of the images and offers more correct moving information of the medical images. The results can be used to aid the identification of the symptom and obtain an acceptable conclusion for clinical diagnosis.
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Shih-Chun, Fan-Chiang. "A study on Segmentation of Remote Sensed Image using Self-Organization Map." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0005-2405200614331200.
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Wang, Jyun-Pin, and 汪均嬪. "Speedup of Color Palette Indexing in Self-Organization of Kohonen Feature Map." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/30896519415654495830.
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碩士<br>國立臺灣科技大學<br>資訊工程系<br>97<br>Based on the self-organization of Kohonen feature map (SOFM), recently, Pei et al. presented an efficient color palette indexing method to construct a color table for compression. Taking the palette indexing method as a representative, this thesis presents two new strategies, the pruning-based search strategy and the lookup table (LUT)-based update strategy, to speed up the learning process in the SOFM. The proposed search strategy is used to speed up the process for finding the winning neuron in each iteration; the proposed LUT-based update strategy is used to speed up the lateral update interaction between the winning neuron and its neighboring neurons in the SOFM. Based on four typical testing images, experimental results demonstrate that our proposed two strategies have 35% execution-time improvement ratio in average. The practical improvement ratio is very close to that in the theoretical analysis.
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Fan-Chiang, Shih-Chun, and 范姜士均. "A study on Segmentation of Remote Sensed Image using Self-Organization Map." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/48251081404710636681.
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碩士<br>國立中興大學<br>資訊科學系所<br>94<br>An image segmentation system based on neural network is proposed for the segmentation of the color and remotely sensed images. This system is facilitated by Kohonen self-organizing map (SOM), it performs the unsupervised segmentation.
In the input layer, traditional SOM only use the pixel values of R, G, and B channels, but it does not consider the relationship existed in the neighborhoods. However, in natural images, the pixels usually have strong correlation with their neighborhoods. For example, the adjacent pixels in an object are generally dependent on each other. Therefore, we propose a modified self-organizing map system is proposed in this thesis; it uses the additional spatial features in the input layer, such as the mean, medium filter, and the discrete cosine transform. In this way, we can consider both pixels themselves and their neighborhood information at the same time. And we also add a new weighting function for each neuron, which can help each neuron to be able to map to a suitable output neuron. Finally, we use application of the noise-filter to improve segmentation quality at the post-processing stage.
Experimental results show that the proposed method can separate successfully the different color texture in the remotely sensed images.
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陳恆惠. "Spatio-Temporal Data Mining Based on Wavelet Transform Using Self-Organization Map." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/21959545824401212102.
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Roussinov, Dmitri G., and Hsinchun Chen. "A Scalable Self-organizing Map Algorithm for Textual Classification: A Neural Network Approach to Thesaurus Generation." 1998. http://hdl.handle.net/10150/106141.
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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>The rapid proliferation of textual and multimedia online databases, digital libraries, Internet servers, and intranet services has turned researchers' and practitioners' dream of creating an information-rich society into a nightmare of info-gluts. Many researchers believe that turning an info-glut into a useful digital library requires automated techniques for organizing and categorizing large-scale information.
This paper presents research in which we sought to develop a scaleable textual classification and categorization system based on the Kohonen's self-organizing feature map (SOM) algorithm. In our paper, we show how self-organization can be used for automatic thesaurus generation.
Our proposed data structure and algorithm took advantage of the sparsity of coordinates in the document input vectors and reduced the SOM computational complexity by several order of magnitude. The proposed Scaleable SOM (SSOM) algorithm makes large-scale textual categorization tasks a possibility. Algorithmic intuition and the mathematical foundation of our research are presented in detail. We also describe three benchmarking experiments to examine the algorithm's performance at various scales: classification of electronic meeting comments, Internet homepages, and the Compendex collection.
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Tangsripairoj, Songsri. "A growing hierarchical self-organizing map with mining association rules for software repository organization and visualization." 2004. http://digital.library.okstate.edu/etd/umi-okstate-1123.pdf.
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Liu, Yi-Lin, and 劉怡麟. "The Study of Image Classification on the Landslide Area through Self-Organization Map and Discrete Rough Sets." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/19909327225468166851.
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碩士<br>中興大學<br>土木工程學系所<br>99<br>Spatial information of a landslide is usually analyzed by statistical analysis based on the topography, vegetation, and landform information. However, due to the uncertainty and the massive amount of data, the determination will result in many misjudgments.
This paper presents a multi-category image classification. The slope of the DEM, normalized difference vegetation index, and other modification of spectrum information are used. The (a) self-organizing map and (b) Discrete Rough Set, are then used to achieve rapidly interpret the landslides.
We selected 30 samples of landslide spectral values surrounding the Wan-da reservoir. Following (a) and (b), the classifier was used to distinguish landslide and non-landslide areas. The results showed the spatial characteristics of the two image classification methods. We also examined eight different indicators of vegetation. We designed a novel method to effectively improve performance classification using the threshold method. Regardless of the slope thresholds, the accuracy could only reach 78%. However, if the thresholds are considered, the accuracy can be improved by about 10%. The multi-categories classification accuracy is about 53%. The transformation rules will enhance 9.5% of accuracy.
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Chung, Wen-Chieh, and 鍾文杰. "Integration of Self-Organization Map and Genetic K-Means Algorithm for Data Mining in Customer Relationship Management." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/62589918349822166197.
Full textAbstract:
碩士<br>國立臺北科技大學<br>生產系統工程與管理研究所<br>89<br>Recently, the subject of Customer Relationship Management is quickly rising. Customer has become an important index for building an enterprise since customer is the source for making direct profit. In addition, the satisfaction of customer seriously influences the enterprise’s future development. Thus, it is critical to use the techniques of data mining in order to help the enterprise get the valuable information for decision support, increase the satisfaction of customer and the profit of enterprise.
Clustering analysis is the common way to analyze the data when making data mining. It can be divided into three methods, Multivariate Analysis, Neural Network and Genetic Algorithm. Especially for Multivariate Analysis, it is the most used method. This study attempts to compare three methods. Firstly, Monte Carlo method generates the simulated data in order to evaluate the three clustering methods: (1)using Self-Organization Map to find the initial clusters, and then feed them into K-Means method (SOM+K-Means)；(2)Kuo’s method which integerates Self-Organization Map and K-Means (SOM+K)；(3)using Self-Organization Map to find the initial clusters, and then feed them to modified Genetic K-Means Algorithm to find the optimal clusters. Finally,using the four famous distribution company domestically to evaluate the effect of three clustering methods for the analysis of segmentation.
Six hundred questionnaires are surveyed, and finally 415 effective questionnaires are obtained. First, using the importance to do clustering analysis, and compare the effect of three clustering tools. The best method is SOM+GKA since it’s within of sum squares (SSW) is the smallest. Then, using the satisfaction to do Factor Analysis, and extract four factors which are credit and access, reliability, convenience, and visibility. Finally, based on the clustering results, this research can provide some suggestions for solving the problems existing in the current Customer Relationship Management.
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Lourenço, Marcos Pratas. "Caracterização da situação orçamental no poder local em indicadores orçamentais e self-organizing map." Master's thesis, 2011. http://hdl.handle.net/10362/5648.
Full textAbstract:
Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação.<br>Com este trabalho pretende-se caracterizar organizações do poder local portuguesas face ao orçamento que dispuseram entre os anos de 2005 e 2008. Esta caracterização será feita recorrendo ao uso de rácios baseados em indicadores orçamentais, informação proveniente do poder local do continente português, região autónoma da Madeira e dos Açores. Pretende-se também o desenvolvimento de uma solução que permita auxiliar no processo de tomada de decisão. A ferramenta que servirá de base à exploração dos dados orçamentais será o Self-Organizing Map (SOM), método usado com o objectivo de não só reduzir a dimensionalidade dos dados como também permitir uma visualização mais fácil através do agrupamento de organizações similares entre si. O SOM, também conhecido por rede Kohonen (Kohonen T. , 2001), é um método computacional para a análise e visualização de dados de grandes dimensões. Os dados para o estudo foram fornecidos pela Direcção Geral do Orçamento sendo compostos por variáveis provenientes da contabilidade orçamental, também denominadas por rácios. Com o uso do SOM pretende-se classificar as organizações face a informação orçamental disponível nos dados deste estudo e, posteriormente, a sua utilização na previsão do comportamento das organizações no futuro. Inicialmente o estudo irá ter como base dados referentes ao ano de 2005 sendo posteriormente aplicado aos anos de 2006, 2007 e 2008.
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Coelho, Inês Capote. "Criação de clusters de clientes de um banco através do self-organizing maps hierárquico: comparação entre HSOM e SOM." Master's thesis, 2013. http://hdl.handle.net/10362/10511.
Full textAbstract:
Trabalho de Projeto apresentado como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação, especialização em Gestão do Conhecimento e Inteligência de Negócio<br>Um dos maiores desafios no processo de clustering está na multidimensionalidade da maioria dos problemas. A descrição de um fenómeno, necessita, normalmente, de um número elevado de variáveis, afetando assim, a eficiência dos algoritmos de clustering. Muitos destes algoritmos são sensíveis a variáveis divergentes, ou seja, com diferentes distribuições. Estas caracterizam-se por apresentar grandes diferenças quanto à tendência geral, o que lhes confere um maior peso na solução final de clustering.
Como forma de contornar este aspeto, surge o HSOM (Hierarchical Self-Organizing Map), o qual permite agrupar as variáveis em vários temas, produzindo assim uma segmentação final onde cada um dos temas usados terá um impacto semelhante na mesma.
Este Trabalho de Projeto vai debruçar-se sobre esta temática e envolve dois principais objetivos. O primeiro numa vertente teórica que visa explicar o método SOM (Self Organizing Maps) juntamente com o HSOM, dando a conhecer o modo como funcionam. O segundo, numa vertente prática, tendo o intuito de aplicar o método HSOM na construção de clusters, com base em clientes de um Banco, agrupando as variáveis nos seguintes temas: Marketing, Produção, Risco e Sociodemográficos.
Com isto, o objetivo final será a construção de uma interface interativa e de fácil utilização que segmente os clientes, de modo a facilitar à obtenção de informação sobre os mesmos, auxiliar as análises futuras e diferenciar as ofertas nas campanhas de marketing direto.
Neste âmbito, tanto o HSOM (com sete clusters) como o SOM (com cinco clusters) são concretizáveis. O HSOM permite reduzir a dimensionalidade dos problemas, a construção de uma estrutura natural do problema e exige menos esforço computacional usando diferentes SOMs para cada nível hierárquico.<br>Dimensionality is one of the biggest challenges faced by clustering process. Usually, the description of a phenomenon requires a large number of variables, which affects the clustering algorithms efficiency. Many of these algorithms are sensitive to variables with large differences between their scales which gives them a bigger weight on the final clustering solution.
HSOM (Hierarchical Self Organizing Maps) deals with this problem by grouping several variables in different subjects, producing a final segmentation where each subject has similar impact.
This project focuses on this subject and has two main goals. Explaining SOM and HSOM methods by introducing them in a theoretical way is the first one. The second one is applying the HSOM and SOM methods in a customer bank database in order to build clusters by grouping the available variables in different subjects – Marketing, Performance, Risk and Socio-demographic.
Thus will be possible to build an interactive and user friendly interface which will allow segmenting customers, making easier information search, supporting future analysis and creating different offers in direct marketing campaigns.
In this context, HSOM (with seven clusters) and SOM (with five clusters) are achievable. The HSOM reduces the dimensionality of the problems, require less computational effort and construct a natural structure of the problem, using different SOMs for each hierarchical level.
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Cordas, Catarina Isabel Agostinho. "Análise Exploratória do Índice de Desenvolvimento Humano: uma aplicação do Self Organizing Map na segmentação dos países." Master's thesis, 2012. http://hdl.handle.net/10362/8791.
Full textAbstract:
Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação.<br>Em 1990 as Nações Unidas através do seu Programa para o Desenvolvimento,
introduziu o Índice de Desenvolvimento Humano, que se tornaria um dos índices mais
utilizados para comunicar o desenvolvimento dos países. Todos os anos este índice é
publicado, classificando os países em quatro categorias: “Desenvolvimento Humano
Baixo”, “Desenvolvimento Humano Médio”, “Desenvolvimento Humano Alto” e
“Desenvolvimento Humano Muito Alto”.
A simplicidade deste índice (média da realização dos países em três dimensões: saúde,
educação e rendimento), associado ao facto de ele defender que o desenvolvimento
humano é mais do que apenas crescimento económico, são a chave do seu sucesso. No
entanto, desde o seu lançamento que este índice tem originado um intenso debate e
algumas críticas, entre as quais a escolha das dimensões de desenvolvimento, deixando
dimensões importantes como a desigualdade (económica e de género) e a liberdade
política.
O objectivo principal da dissertação que me proponho realizar é aplicar uma técnica de
Data Mining, mais concretamente a rede neuronal Self Organizing Map, na
segmentação dos países que compõem o Relatório do Desenvolvimento Humano das
Nações Unidas, permitindo desta forma segmentar os países utilizando mais dimensões
do que as actualmente utilizadas pelo Índice de Desenvolvimento Humano. Os dados
utilizados estão publicados no website do Programa para o Desenvolvimento das
Nações Unidas e será utilizado um software desenvolvido pelo ISEGI que aplica o
algoritmo, o GeoSOM Suit. Os resultados obtidos sugerem que há diferenças de
classificação de alguns países quando aplicadas outras dimensões de desenvolvimento
humano, nomeadamente Empowerment, Desigualdade e Sustentabilidade.
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Villar, João Felipe Campos. "Relação entre as variáveis sociais, económicas e ambientais com o padrão da distribuição espaço-temporal dos casos de Dengue por munícipio no Brasil : de 2008 até 2012, utilizando o SOM." Master's thesis, 2015. http://hdl.handle.net/10362/16060.
Full textAbstract:
Atualmente, um dos principais desafios que afeta a saúde pública no Brasil é a crescente evolução no número de casos e epidemias provocados pelo vírus da dengue. Não existem estudos suficientes que consigam elucidar quais fatores contribuem para a evolução das epidemias de Dengue. Fatores como condições sanitárias, localização geográfica, investimentos financeiros em infraestrutura e qualidade de vida podem estar relacionados com a incidência de Dengue. Além disso, outra questão que merece um maior destaque é o estudo para se identificar o grau de impacto das variáveis determinantes da dengue e se existe um padrão que está correlacionado com a taxa de incidência. Desta forma, este trabalho tem como objetivo principal a correlação da taxa de incidência da dengue na população de cada município brasileiro, utilizando dados relativos aos aspectos sociais, econômicos, demográficos e ambientais. Outra contribuição relevante do trabalho, foi a análise dos padrões de distribuição espacial da taxa de incidência de Dengue e sua relação com os padrões encontrados utilizando as variáveis socioeconômicas e ambientais, sobretudo analisando a evolução temporal no período de 2008 até 2012. Para essa análises, utilizou-se o Sistema de Informação Geográfica (SIG) aliado com a mineração de dados, através da metodologia de rede neural mais especificamente o mapa auto organizável de Kohonen ou self-organizing maps (SOM). Tal metodologia foi empregada para a identificação de padrão de agrupamentos dessas variáveis e sua relação com as classes de incidência de dengue no Brasil (Alta, Média e Baixa). Assim, este projeto contribui de forma significativa para uma melhor compreensão dos fatores que estão associados à ocorrência de Dengue, e como essa doença está correlacionada com fatores como: meio ambiente, infraestrutura e localização no espaço geográfico.
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Gorricha, Jorge Manuel Lourenço. "Exploratory data analysis using self-organising maps defined in up to three dimensions." Doctoral thesis, 2015. http://hdl.handle.net/10362/17852.
Full textAbstract:
The SOM is an artificial neural network based on an unsupervised learning process that performs a nonlinear mapping of high dimensional input data onto an ordered and structured array of nodes, designated as the SOM output space. Being simultaneously a quantization algorithm and a projection algorithm, the SOM is able to summarize and map the data, allowing its visualization. Because using the most common visualization methods it is very difficult or even impossible to visualize the SOM defined with more than two dimensions, the SOM output space is generally a regular two dimensional grid of nodes. However, there are no theoretical problems in generating SOMs with higher dimensional output spaces. In this thesis we present evidence that the SOM output space defined in up to three dimensions can be used successfully for the exploratory analysis of spatial data, two-way data and three-way data. Although the differences between the methods that are proposed to visualize each group of data, the approach adopted is commonly based in the projection of colour codes, which are obtained from the output space of 3D SOMs, in some specific bi-dimensional surface, where data can be represented according to its own characteristics. This approach is, in some cases, also complemented with the simultaneous use of SOMs defined in one and two dimensions, so that patterns in data can be properly revealed. The results obtained by using this visualization strategy indicates not only the benefits of using the SOM defined in up to three dimensions but also shows the relevance of the combined and simultaneous use of different models of the SOM in exploratory data analysis.
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Yao, Yu-Chi, and 游玉麒. "A Study on the construction of a Tamdem Automated Guided Vehicle System by Using the concept of Self-Organizing Map (SOM) neural network." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/62897117015211981253.
Full textAbstract:
碩士<br>國立雲林科技大學<br>工業工程與管理研究所碩士班<br>90<br>This research proposes a basic designing structure of the tandem automated guided vehicle (AGV) system. The main structure consists of two stages as follows:
Stage Ι：Work stations partition
In this stage, the concept of Self-Organizing Map (SOM) neural network will be use
to develop a partitioning scheme to configure tandem AGV system. During the work stations partitioning stage, the total workload of the AGV system will be considered. The partitioning scheme is aimed to minimize the AGV’s workload, which include flow within a zone and flow between zones.
Stage Π：Formation of transportation system
The concept of transportation center will be used in this stage. In constructing transfer
system, this research develop a procedure to find the optimal location of transfer
point. This procedure considers the flow of each zone and the distance between each
transfer point. We hope the decision of each transfer point is based on the view of the
system, not based on the view of a specific zone.
Finally a numerical example will be used to illustrate the all procedures developing in this research. A simulation case study is also presented to compare the performance of the transportation system with other papers.
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Gorricha, Jorge Manuel Lourenço. "Visualization of clusters in geo-referenced data using three-dimensional self-organizing maps." Master's thesis, 2010. http://hdl.handle.net/10362/2631.
Full textAbstract:
Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação<br>The Self-Organizing Map (SOM) is an artificial neural network that performs simultaneously vector quantization and vector projection. Due to this characteristic, the SOM is an effective method for clustering analysis via visualization. The SOM can be visualized through the output space, generally a regular two-dimensional grid of nodes, and through the input space, emphasizing the vector quantization process. Among all the strategies for visualizing the SOM, we are particularly interested in those that allow dealing with spatial dependency, linking the SOM to the geographic visualization with color. One possible approach, commonly used, is the cartographic representation of data with label colors defined from the output space of a two-dimensional SOM. However, in the particular case of geo-referenced data, it is possible to consider the use of a three-dimensional SOM for this purpose, thus adding one more dimension in the analysis. In this dissertation is presented a method for clustering geo-referenced data that integrates the visualization of both perspectives of a three dimensional SOM: linking its output space to the cartographic representation through a ordered set of colors; and exploring the use of frontiers among geo-referenced elements, computed according to the distances in the input space between their Best Matching Units.
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Windisch, Sven. "Entwurf einer lernfähigen selbst-organisierenden Karte (SOM) in SystemC zur Realisierung in einem eingebetteten System." 2008. https://ul.qucosa.de/id/qucosa%3A17203.
Full textAbstract:
Im Bereich der medizinischen Geräte haben eingebettete Systeme verstärkt Einzug gehalten. Nicht nur in Operationssälen oder Intensivstationen, auch im Bereich der Prothesensteuerung spielt
moderne Computertechnik in zunehmendem Maße eine Rolle, auch und insbesondere im Bereich der Prothesensteuerung durch elektronisch vorverarbeitete Nervensignale. Die zur Signalverarbeitung eingesetzte, vortrainierte selbst-organisierende Karte stößt jedoch auf das Problem, sich den verändernden Gegebenheiten in den Nervensignalen des Patienten nicht anpassen zu können. In dieser Arbeit wird die Möglichkeit untersucht, die Steuerung der Handprothese mit einer Nachlernfunktion auszustatten, um während des Einsatzes der Prothese auf die Veränderungen der Nervensignale des Patienten reagieren zu können. Da diese Veränderungen höchst individuell verlaufen, werden Parameter eingeführt, mit denen das Nachlernverfahren an die Gegebenheiten des Patienten angepasst werden kann. Verschiedene denkbare Lernstrategien werden untersucht und hinsichtlich ihrer Effizienz und ihrer Aktualität bewertet. Um die Verwendbarkeit der Implementierung sicherzustellen, muss darauf geachtet werden, dass der entstehende SystemC-Code keine Elemente des nicht synthetisierbaren Subsets enthält. Zusätzlich wird die Synthetisierbarkeit mit dem Agility-Compiler untersucht.
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Antunes, Jorge Manuel Alves. "Contributions towards smart cities : exploring block level census data for the characterization of change in Lisbon." Master's thesis, 2016. http://hdl.handle.net/10362/17446.
Full textAbstract:
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management<br>The interest in using information to improve the quality of living in large urban areas and its governance efficiency has been around for decades. Nevertheless, the improvements in Information and Communications Technology has sparked a new dynamic in academic research, usually under the umbrella term of Smart Cities. This concept of Smart City can probably be translated, in a simplified version, into cities that are lived, managed and developed in an information-saturated environment. While it makes perfect sense and we can easily foresee the benefits of such a concept, presently there are still several significant challenges that need to be tackled before we can materialize this vision. In this work we aim at providing a small contribution in this direction, which maximizes the relevancy of the available information resources. One of the most detailed and geographically relevant information resource available, for the study of cities, is the census, more specifically the data available at block level (Subsecção Estatística). In this work, we use Self-Organizing Maps (SOM) and the variant Geo-SOM to explore the block level data from the Portuguese census of Lisbon city, for the years of 2001 and 2011. We focus on gauging change, proposing ways that allow the comparison of the two time periods, which have two different underlying geographical bases. We proceed with the analysis of the data using different SOM variants, aiming at producing a two-fold portrait: one, of the evolution of Lisbon during the first decade of the XXI century, another, of how the census dataset and SOM’s can be used to produce an informational framework for the study of cities.
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Dean, Eileen J. "Computer aided identification of biological specimens using self-organizing maps." Diss., 2010. http://hdl.handle.net/2263/23116.
Full textAbstract:
For scientific or socio-economic reasons it is often necessary or desirable that biological material be identified. Given that there are an estimated 10 million living organisms on Earth, the identification of biological material can be problematic. Consequently the services of taxonomist specialists are often required. However, if such expertise is not readily available it is necessary to attempt an identification using an alternative method. Some of these alternative methods are unsatisfactory or can lead to a wrong identification. One of the most common problems encountered when identifying specimens is that important diagnostic features are often not easily observed, or may even be completely absent. A number of techniques can be used to try to overcome this problem, one of which, the Self Organizing Map (or SOM), is a particularly appealing technique because of its ability to handle missing data. This thesis explores the use of SOMs as a technique for the identification of indigenous trees of the Acacia species in KwaZulu-Natal, South Africa. The ability of the SOM technique to perform exploratory data analysis through data clustering is utilized and assessed, as is its usefulness for visualizing the results of the analysis of numerical, multivariate botanical data sets. The SOM’s ability to investigate, discover and interpret relationships within these data sets is examined, and the technique’s ability to identify tree species successfully is tested. These data sets are also tested using the C5 and CN2 classification techniques. Results from both these techniques are compared with the results obtained by using a SOM commercial package. These results indicate that the application of the SOM to the problem of biological identification could provide the start of the long-awaited breakthrough in computerized identification that biologists have eagerly been seeking.<br>Dissertation (MSc)--University of Pretoria, 2011.<br>Computer Science<br>unrestricted