Academic literature on the topic 'Classification and discrimination; cluster analysis'
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Journal articles on the topic "Classification and discrimination; cluster analysis"
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Okuyama, Shuji, and Toshiyuki Mitsui. "Discrimination of Marihuana Using Cluster Analysis." Journal of Chemical Software 2, no. 4 (1995): 220–27. http://dx.doi.org/10.2477/jchemsoft.2.220.
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Razak, Abdul, and Nirmala C. R. "A computing model for trend analysis in stock data stream classification." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (September 1, 2020): 1602. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1602-1609.
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<p>For several decades, many statistical and scientific efforts took place for the better analysis or prediction of stock trading. But still it is open to offer new avenues for the scientists to rethink and discover new inferences by adopting latest technological scenarios. In this regard, this paper is trying to apply classification techniques on stock data stream through feature extraction for the trend analysis. The proposed work is involving k-means for clustering samples into two clusters (the stocks in trend as one cluster and another on as stocks not in trend). The trend analysis is done based on density estimation of the stocks with respect to sectors. A well-known data representation method that is histogram is used to represent the sector which is in trend. This work has been implemented and experimented by considering live NSE (india) data using python and its related tools.</p>
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Goncharenko, I. V. "DRSA: a non-hierarchical clustering algorithm using k-NN graph and its application in vegetation classification." Vegetation of Russia, no. 27 (2015): 125–38. http://dx.doi.org/10.31111/vegrus/2015.27.125.
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In this article we proposed a new method of non-hierarchical cluster analysis using k-nearest-neighbor graph and discussed it with respect to vegetation classification. The method of k-nearest neighbor (k-NN) classification was originally developed in 1951 (Fix, Hodges, 1951). Later a term “k-NN graph” and a few algorithms of k-NN clustering appeared (Cover, Hart, 1967; Brito et al., 1997). In biology k-NN is used in analysis of protein structures and genome sequences. Most of k-NN clustering algorithms build «excessive» graph firstly, so called hypergraph, and then truncate it to subgraphs, just partitioning and coarsening hypergraph. We developed other strategy, the “upward” clustering in forming (assembling consequentially) one cluster after the other. Until today graph-based cluster analysis has not been considered concerning classification of vegetation datasets.
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Yamamuro, Tadashi, Kenji Kuwayama, Kenji Tsujikawa, Tatsuyuki Kanamori, Yuko T. Iwata, and Hiroyuki Inoue. "Study of cannabis discrimination by hierarchical cluster analysis." Japanese Journal of Forensic Science and Technology 21, no. 2 (2016): 109–24. http://dx.doi.org/10.3408/jafst.709.
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ZHANG, JieJin, Hao WU, Liang ZHAO, YueYue LI, GuiChen ZHOU, ZhenYu ZHU, and GuoQing ZHANG. "Discrimination of Moutan Cortex by principal component analysis and cluster analysis." Pharmaceutical Care and Research 13, no. 6 (December 30, 2013): 449–51. http://dx.doi.org/10.5428/pcar20130617.
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Steinhorst, R. Kirk, and Roy E. Williams. "Discrimination of Groundwater Sources Using Cluster Analysis, MANOVA, Canonical Analysis and Discriminant Analysis." Water Resources Research 21, no. 8 (August 1985): 1149–56. http://dx.doi.org/10.1029/wr021i008p01149.
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Plichta, Anna. "Methods of Classification of the Genera and Species of Bacteria Using Decision Tree." Journal of Telecommunications and Information Technology 4, no. 2019 (December 30, 2019): 74–82. http://dx.doi.org/10.26636/jtit.2019.137419.
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This paper presents a computer-based method for recognizing digital images of bacterial cells. It covers automatic recognition of twenty genera and species of bacteria chosen by the author whose original contribution to the work consisted in the decision to conduct the process of recognizing bacteria using the simultaneous analysis of the following physical features of bacterial cells: color, size, shape, number of clusters, cluster shape, as well as density and distribution of the cells. The proposed method may be also used to recognize the microorganisms other than bacteria. In addition, it does not require the use of any specialized equipment. The lack of demand for high infrastructural standards and complementarity with the hardware and software widens the scope of the method’s application in diagnostics, including microbiological diagnostics. The proposed method may be used to identify new genera and species of bacteria, but also other microorganisms that exhibit similar morphological characteristics.
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Lin, Gwo-Fong, and Chun-Ming Wang. "Performing cluster analysis and discrimination analysis of hydrological factors in one step." Advances in Water Resources 29, no. 11 (November 2006): 1573–85. http://dx.doi.org/10.1016/j.advwatres.2005.11.008.
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Gonzalez-Escalona, Narjol, Ruth Timme, Brian H. Raphael, Donald Zink, and Shashi K. Sharma. "Whole-Genome Single-Nucleotide-Polymorphism Analysis for Discrimination of Clostridium botulinum Group I Strains." Applied and Environmental Microbiology 80, no. 7 (January 24, 2014): 2125–32. http://dx.doi.org/10.1128/aem.03934-13.
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ABSTRACTClostridium botulinumis a genetically diverse Gram-positive bacterium producing extremely potent neurotoxins (botulinum neurotoxins A through G [BoNT/A-G]). The complete genome sequences of three strains harboring only the BoNT/A1 nucleotide sequence are publicly available. Although these strains contain a toxin cluster (HA+OrfX−) associated with hemagglutinin genes, little is known about the genomes of subtype A1 strains (termed HA−OrfX+) that lack hemagglutinin genes in the toxin gene cluster. We sequenced the genomes of three BoNT/A1-producingC. botulinumstrains: two strains with the HA+OrfX−cluster (69A and 32A) and one strain with the HA−OrfX+cluster (CDC297). Whole-genome phylogenic single-nucleotide-polymorphism (SNP) analysis of these strains along with other publicly availableC. botulinumgroup I strains revealed five distinct lineages. Strains 69A and 32A clustered with theC. botulinumtype A1 Hall group, and strain CDC297 clustered with theC. botulinumtype Ba4 strain 657. This study reports the use of whole-genome SNP sequence analysis for discrimination ofC. botulinumgroup I strains and demonstrates the utility of this analysis in quickly differentiatingC. botulinumstrains harboring identical toxin gene subtypes. This analysis further supports previous work showing that strains CDC297 and 657 likely evolved from a common ancestor and independently acquired separate BoNT/A1 toxin gene clusters at distinct genomic locations.
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Crawford, I., S. Ruske, D. O. Topping, and M. W. Gallagher. "Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol." Atmospheric Measurement Techniques Discussions 8, no. 7 (July 16, 2015): 7303–33. http://dx.doi.org/10.5194/amtd-8-7303-2015.
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Abstract. In this paper we present improved methods for discriminating and quantifying Primary Biological Aerosol Particles (PBAP) by applying hierarchical agglomerative cluster analysis to multi-parameter ultra violet-light induced fluorescence (UV-LIF) spectrometer data. The methods employed in this study can be applied to data sets in excess of 1×106 points on a desktop computer, allowing for each fluorescent particle in a dataset to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient dataset. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4) where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best performing methods were applied to the BEACHON-RoMBAS ambient dataset where it was found that the z-score and range normalisation methods yield similar results with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP) where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of bacterial aerosol concentration by a factor of 5. We suggest that this likely due to errors arising from misatrribution due to poor centroid definition and failure to assign particles to a cluster as a result of the subsampling and comparative attribution method employed by WASP. The methods used here allow for the entire fluorescent population of particles to be analysed yielding an explict cluster attribution for each particle, improving cluster centroid definition and our capacity to discriminate and quantify PBAP meta-classes compared to previous approaches.
Dissertations / Theses on the topic "Classification and discrimination; cluster analysis"
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Dannenberg, Matthew. "Pattern Recognition in High-Dimensional Data." Scholarship @ Claremont, 2016. https://scholarship.claremont.edu/hmc_theses/76.
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Vast amounts of data are produced all the time. Yet this data does not easily equate to useful information: extracting information from large amounts of high dimensional data is nontrivial. People are simply drowning in data. A recent and growing source of high-dimensional data is hyperspectral imaging. Hyperspectral images allow for massive amounts of spectral information to be contained in a single image. In this thesis, a robust supervised machine learning algorithm is developed to efficiently perform binary object classification on hyperspectral image data by making use of the geometry of Grassmann manifolds. This algorithm can consistently distinguish between a large range of even very similar materials, returning very accurate classification results with very little training data. When distinguishing between dissimilar locations like crop fields and forests, this algorithm consistently classifies more than 95 percent of points correctly. On more similar materials, more than 80 percent of points are classified correctly. This algorithm will allow for very accurate information to be extracted from these large and complicated hyperspectral images.
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Caetano, Luis Augusto Martins. "Impacto da intensidade de pastejo na produtividade da soja em integração com bovinos de corte." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/164063.
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A intensidade de pastejo pode ser reconhecida como primordial na formação do potencial produtivo da lavoura em sistemas integrados de produção agropecuária (SIPA). Neste estudo, objetivamos entender como diferentes intensidades de pastejo definem a produtividade da soja em SIPA. O trabalho está inserido em um protocolo experimental de longa duração iniciado em 2001, no estado do Rio Grande do Sul, Brasil. Os tratamentos são definidos durante a fase pastagem, arranjados em um delineamento de blocos completamente casualizados com três repetições em diferentes intensidades de pastejo por novilhos em pasto misto de azevém anual e aveia: pastejo intenso (P10), pastejo moderado (P20), pastejo moderado-leve (P30), pastejo leve (P40) e ausência de pastejo (SP) e ausência de pastejo (SP). A soja foi semeada após a saída dos animais e os dados coletadas durante a safra 2015/16. A inserção do animal não afetou a produtividade da soja (P=0,0570). Análises de cluster e discriminante foram utilizadas no estudo da variação na produtividade. O cluster hierárquico agrupou valores de produtividade em três grupos, alta (CA), intermediária (CI) e baixa (CB), baseado em suas similaridades. Os atributos químicos não foram limitantes da produtividade. O residual da pastagem exerceu papel central na construção do potencial produtivo (P<0,001). A maior população de plantas foi encontrada no CB (P<0,001), com número inferior de legumes por área, frente aos demais cluster (P<0,001). A plasticidade fenotípica da soja explica a compensação pela modificação da arquitetura do estande de plantas nos clusters CA e CI, resultando na maior formação de legumes por planta (P<0,05). Pela análise de cluster observou-se que o P10 não apresenta valores de alta produtividade ao contrário do SP que não possui valores de produtividade baixa. Quase metade dos valores observado no P40 são mais produtividade enquanto, o P20 e P30 tendem a apresentar maior homogeneidade na distribuição entre os clusters. A análise discriminante definiu parâmetros que mais explicam a variação entre os clusters Através da função stepwise encontramos cinco variáveis (residual da pastagem, fósforo, altura final - R8, nós por ramos, legumes por área) que explicam a maioria da variação. O modelo para predizer a produtividade da soja, a partir das variáveis explicativas avaliadas a campo selecionadas pela análise discriminante, demostrou a acurácia na predição da produtividade espacial, em sistemas integrados de produção agropecuária com diferentes intensidades de pastejo, sob plantio direto na palha.Grazing intensity can be considered essential to the potential productivity of crops in integrated crop-livestock systems (ICLS). In this study, we aimed to understand how differents grazing intensity defines the subsequent productivity of soybeans in ICLS. The work forms part of a long-term experimental protocol dating from 2001 in Rio Grande do Sul state, Brazil. Treatments were defined during the pasture phase, arranged in a completely randomized block design with three replicates under different grazing intensities by steers on Italian ryegrass and black oat mixed pastures: intense grazing (G10), moderate grazing (G20), moderate-light grazing (G30), light grazing (P40), and an ungrazed control (UG). Soybean was planted after exit of the animals and data were collected during the 2015/16 season. The integration of animals did not affect soybean productivity (P=0.0570). Cluster and discrimination analyses were used to study variation in productivity. The cluster hierarchy returned three groups of productivity values: high (HC), intermediate (IC), and low (LC), based on their similarities. Chemical attributes were not limiting to productivity. Pasture residue played a central role in the determination of potential productivity (P<0.001). The greatest plant population was found in the LC (P<0.001), while more pods per plant were found in the HC and IC (P<0.001). This result is explained by the phenotypic plasticity of soybean, which allows it to compensate for lower plant population by modifying plant stand architecture. The cluster analysis revealed that G10 did not present high productivity values, as opposed to UG, which did not contain low productivity values. Nearly half of the values observed in G40 were more productive, while G20 and G30 tended to produce greater homogeneity in the distribution among clusters. A discrimination analysis defined the parameters that explain the greatest part of the variation among clusters. Using a stepwise function, we found five variables (pasture residue, phosphorus, final plant height at R8, nodes per branch, and pods per area) that explained most of the variation. The model to predict soybean productivity, using the explanatory field variables selected by the discrimination analysis, demonstrated accuracy in the prediction of spatial productivity in ICLS with different grazing intensities under no-till.
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Lilliehöök, Hampus. "Extraction of word senses from bilingual resources using graph-based semantic mirroring." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-91880.
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In this thesis we retrieve semantic information that exists implicitly in bilingual data. We gather input data by repeatedly applying the semantic mirroring procedure. The data is then represented by vectors in a large vector space. A resource of synonym clusters is then constructed by performing K-means centroid-based clustering on the vectors. We evaluate the result manually, using dictionaries, and against WordNet, and discuss prospects and applications of this method.I det här arbetet utvinner vi semantisk information som existerar implicit i tvåspråkig data. Vi samlar indata genom att upprepa proceduren semantisk spegling. Datan representeras som vektorer i en stor vektorrymd. Vi bygger sedan en resurs med synonymkluster genom att applicera K-means-algoritmen på vektorerna. Vi granskar resultatet för hand med hjälp av ordböcker, och mot WordNet, och diskuterar möjligheter och tillämpningar för metoden.
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Nievas, Lio Estefanía. "Aplicando máquinas de soporte vectorial al análisis de pérdidas no técnicas de energía eléctrica." Bachelor's thesis, 2016. http://hdl.handle.net/11086/3946.
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Tesis (Lic. en Matemática)--Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, 2016.Las pérdidas no técnicas en la distribución de energía eléctrica generan grandes gastos a las empresas encargadas de prestar el servicio de energía eléctrica y son extremadamente difíciles de detectar. En este proyecto se usa una técnica de aprendizaje automático (más conocida como Machine Learning) basada en máquinas de soporte vectorial (SVM, siglas en inglés de Support Vector Machine) para poder clasificar, de la manera más confiable posible, a los usuarios de la red en dos grupos diferenciados: los que cometen fraude y los que no. El entrenamiento se realiza a partir de una base de datos ya clasificada y tomando en cuenta el consumo de los usuarios a lo largo de un período de tiempo. Tales datos, en este proyecto, serán de usuarios de la ciudad de Córdoba. En nuestro trabajo implementaremos un algoritmo que construya el clasificador y luego analizaremos su confiabilidad clasificando a consumidores de la ciudad que han sido sometidos a una auditoría. Luego de obtener un clasificador confiable el mismo servirá para detectar posibles fraudes de los usuarios.
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Chun-Ming, Wang. "Integration of cluster analysis and discrimination analysis using self-organizing map." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2112200521430300.
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Chou, Shih-Cheng, and 丘世健. "The Cross-Validation of Cluster and Discrimination Analysis And the Study of Bayesian Decision Rule - Businesses Groups Case." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/24510461932664438329.
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Richards, Larissa Christine. "Chemometric analysis of full scan direct mass spectrometry data for the discrimination and source apportionment of atmospheric volatile organic compounds measured from a moving vehicle." Thesis, 2021. http://hdl.handle.net/1828/13333.
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Anthropogenic emissions into the troposphere can impact air quality, leading to poorer health outcomes in the affected areas. Volatile organic compounds (VOCs) are a group of chemical compounds, including some which are toxic, that are precursors in the formation of ground-level ozone and secondary organic aerosols. VOCs have a variety of sources, and the distribution of atmospheric VOCs differs significantly over time and space. Historically, the large number of chemical species present at low concentrations (parts-per-trillion to parts-per-billion by volume) have made VOCs difficult to measure in ambient air. However, with improvements in analytical instrumentation, these measurements are becoming more common place. Direct mass spectrometry (MS), such as membrane introduction mass spectrometry (MIMS) and proton-transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) facilitate real-time, continuous measurements of VOCs in air, with full scan mass spectral data capturing changes in chemical composition with high temporal resolution. Operated on-road, mobilized direct MS has been used for quantitative mapping of VOCs at the neighborhood scale, but identifying VOC sources based on the observed mixture of molecules in the full scan MS dataset has yet to be explored. This dissertation describes the use of chemometric techniques to interrogate full scan MS data, and the progression from discriminating VOC samples of known chemical composition based on full scan MIMS data through to the apportionment of VOC sources measured continuously with a PTR-ToF-MS system operating in a moving vehicle. Lab‐constructed VOC samples of known chemical composition and concentration demonstrated the use of principal component analysis (PCA) to discriminate, and k-nearest neighbours to classify, samples based on normalized full scan MIMS data. Furthermore, multivariate curve resolution-alternating least squares (MCR-ALS) was used to resolve mixtures into molecular component contributions. PCA was also used to discriminate ‘real-world’ VOC mixtures (e.g., woodsmoke VOCs, headspace above aqueous hydrocarbon samples) of unknown chemical composition measured by MIMS. Using vehicle mounted MIMS and PTR-ToF-MS systems, full scan MS data of ambient atmospheric VOCs were collected and PCA was applied to the normalized full scan MS data. A supervised analysis performed PCA on samples collected near known VOC sources, while an unsupervised analysis using PCA followed by cluster analysis was used to identify groups in a continuous, time series PTR-ToF-MS dataset measured between Nanaimo and Crofton, British Columbia (BC). In both the supervised and unsupervised analysis, samples impacted by emissions from different sources (e.g., internal combustion engines, sawmills, composting facilities, pulp mills) were discriminated. With PCA, samples were discriminated based on differences in the observed full scan MS data, however real-world samples are often impacted by multiple VOC sources. MCR-weighted ALS (MCR-WALS) was applied to the continuous, time series PTR-ToF-MS data from three field campaigns on Vancouver Island, BC for source apportionment. Variable selection based on signal-to-noise ratios was used to reduce the mass list while retaining the observed m/z that capture changes in the mixture of VOCs measured, improving model results, and reducing computation time. Both point (e.g., anthropogenic hydrocarbon emissions, pulp mill emissions) and diffuse (e.g., VOCs from forest fire smoke) VOC sources were identified in the data, and were apportioned to determine their contributions to the measured samples. The data analyzed captured fine scale changes in the ambient VOCs present in the air, and geospatial maps of each individual source, and of the source apportionment were used to visualize the distribution of VOC sources across the sampling area. This work represents the first use of MCR-WALS to identify and apportion ambient VOC sources based on continuous PTR-ToF-MS data measured from a moving vehicle. The methods described can be applied to larger scale field campaigns for the source apportionment of VOCs across multiple days to capture diurnal and seasonal variations. Identifying spatial and temporal trends in the sources of VOCs at the regional scale can help to identify pollution ‘hot spots’ and inform evidence-based public policy for improving air quality.Graduate
2022-08-17
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Agnelli, Juan Pablo. "Estimación de parámetros y clasificación de datos : aplicaciones biomédicas." Doctoral thesis, 2011. http://hdl.handle.net/11086/158.
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Tesis (Doctor en Matemática)--Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física, 2011.En esta tesis se proponen principalmente dos tipos de aplicaciones biomédicas para las cuales hemos empleado diferentes herramientas matemáticas y por lo cual el trabajo está dividido en dos partes. En la primera parte nos hemos abocado a la detección de tumores. El objetivo aquí fue estimar la localización, tamaño y parámetros térmicos asociados a un tumor utilizando como información perfiles de temperaturas medidos sobre la superficie corporal. En la segunda parte del trabajo, el objetivo fue desarrollar un algoritmo capaz de extraer, de una gran base de datos, información que reside de manera implícita en estos. Dicha información es previamente desconocida y puede resultar útil para describir el proceso o fenómeno que está bajo análisis o estudio. En particular, aquí se aplicó para la clasificación de distintos tipos de tumores usando como base de datos niveles de expresión genética.
In this thesis we propose two main areas of study, so the work is divided into two parts. The first one is related with tumor location and estimation of parameters related with tumor regions and the second part is concerned with the development of an algorithm for tumor classification from gene expression levels. In the first situation the goal is to estimate position, size and thermal parameters of a tumor using temperature profiles that have been measured on the top boundary of the domain using a thermography camera. From the mathematical point of view the study of these problems imply to pose and analyze inverse problems and also to develop numerical methods to solve it. In a first stage, we use partial differential equations to model heat transfer in living tissue, more precisely we consider the stationary Pennes equation with mixed boundary conditions. For this elliptical equation we have proved existence and uniqueness of the solution and to solve this direct problem a finite difference scheme of second order is considered. Then, to solve the inverse problems these problems were reformulated as optimization problems and to solve these new problems two different methodologies will be presented. The first one, is based on the use of the Patter Search algorithm. This is a direct search algorithm, so it does not make use of derivatives and therefore is very easy to implement. The second methodology that we present makes use of the information provided by the derivative of the function to minimize with respect to the different variables to be estimated. To calculate this derivative we consider some sensitivity analysis tools. In the second part of the work, the goal is to build an algorithm capable to extract, from a large database, useful information that resides implicitly. This information is previously unknown and may be useful to describe the process or phenomenon that is under analysis or study. In particular, here we are interested in classify different types of tumors using gene expression levels. The proposed methodology is based on three main ingredients: 1)the blurring of distinctions between training and testing populations, through the soft assignment of the latter to classes, in an expectation-maximization framework, 2) a procedure for density estimation through a descent flow, that transforms the original distribution into an isotropic Gaussian distribution and 3) a measure of the clustering capability of a set of variables, which leads to an effective procedure for variable selection. The methodology is particularly useful in situations where there are relatively few observations for a phenomenon that is described by a large amount of variables, and no a priori knowledge that strongly links a small subset of these variables to the classification sought. According to the results obtained the methodologies proposed in the first part of this work can be considered as a potential tool to locate tumor regions, like nodular melanomas, as well as to estimate parameters associated with them that could be useful and important to study the tumor evolution after a treatment procedure. The same conclusion applies to the methodology developed in the second part in order to diagnose, prevent and treat different diseases based on gene expression levels.
Juan Pablo Agnelli.
Estimación de parámetros asociados a tumores -- Modelo matemático -- Problemas inversos -- Introducción al análisis de sensibilidad -- Clasificación y agrupamiento de datos -- Estimación de densidades -- Elección de varialbes y evaluación del agrupamiento -- Ejemplos clínicos : clasificación de tumores.
Book chapters on the topic "Classification and discrimination; cluster analysis"
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Iam-On, Natthakan, and Tossapon Boongoen. "Soft Subspace Clustering for Cancer Microarray Data Analysis." In Global Trends in Intelligent Computing Research and Development, 131–45. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4936-1.ch006.
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A need has long been identified for a more effective methodology to understand, prevent, and cure cancer. Microarray technology provides a basis of achieving this goal, with cluster analysis of gene expression data leading to the discrimination of patients, identification of possible tumor subtypes, and individualized treatment. Recently, soft subspace clustering was introduced as an accurate alternative to conventional techniques. This practice has proven effective for high dimensional data, especially for microarray gene expressions. In this review, the basis of weighted dimensional space and different approaches to soft subspace clustering are described. Since most of the models are parameterized, the application of consensus clustering has been identified as a new research direction that is capable of turning the difficulty with parameter selection to an advantage of increasing diversity within an ensemble.
Conference papers on the topic "Classification and discrimination; cluster analysis"
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Georgieva, Olga, Sergey Milanov, and Petia Georgieva. "Cluster analysis for EEG biosignal discrimination." In 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA). IEEE, 2013. http://dx.doi.org/10.1109/inista.2013.6577646.
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Shi, You-ming, Cui-qiong Yan, Dong-yu Li, and Gang Liu. "Discrimination of Amanita Mushrooms Using Fourier Transform Infrared Difference Spectroscopy and Cluster Analysis." In 2011 Symposium on Photonics and Optoelectronics (SOPO 2011). IEEE, 2011. http://dx.doi.org/10.1109/sopo.2011.5780668.
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Li, Ning, Yan Wang, and Kexin Xu. "Fast discrimination of danshen from different geographical areas by NIR spectroscopy and advanced cluster analysis method." In Fourth International Conference on Photonics and Imaging in Biology and Medicine, edited by Kexin Xu, Qingming Luo, Da Xing, Alexander V. Priezzhev, and Valery V. Tuchin. SPIE, 2006. http://dx.doi.org/10.1117/12.710981.
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Botacin, Marcus, André Grégio, and Paulo De Geus. "Malware Variants Identification in Practice." In Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação, 2019. http://dx.doi.org/10.5753/sbseg.2019.13960.
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Malware are persistent threats to computer systems and analysis procedures allow developing countermeasures to them. However, as samples are spreading on growing rates, malware clustering techniques are required to keep analysis procedures scalable. Current clustering approaches use Call Graphs (CGs) to identify polymorphic samples, but they consider only individual functions calls, thus failing to cluster malware variants created by replacing sample's original functions by semantically-equivalent ones. To solve this problem, we propose a behavior-based classification procedure able to group functions on classes, thus reducing analysis procedures costs. We show that classifying samples according their behaviors (via function call semantics) instead by their pure API invocation is a more effective way to cluster malware variants. We also show that using a continence metric instead of a similarity metric helps to identify malware variants when a sample is embedded in another.
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Shaaban, Noha, Fukuzo Masuda, and Hidetsugu Morota. "A New Waveform Signal Processing Method Based on Adaptive Clustering-Genetic Algorithms." In 14th International Conference on Nuclear Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/icone14-89866.
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We present a fast digital signal processing method for numerical analysis of individual pulses from CdZnTe compound semiconductor detectors. Using Maxi-Mini Distance Algorithm and Genetic Algorithms based discrimination technique. A parametric approach has been used for classifying the discriminated waveforms into a set of clusters each has a similar signal shape with a corresponding pulse height spectrum. A corrected total pulse height spectrum was obtained by applying a normalization factor for the full energy peak for each cluster with a highly improvements in the energy spectrum characteristics. This method applied successfully for both simulated and real measured data, it can be applied to any detector suffers from signal shape variation.