Relevant bibliographies by topics / Growing Self Organizing Map

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Academic literature on the topic 'Growing Self Organizing Map'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Growing Self Organizing Map"

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Allahyar, Amin, Hadi Sadoghi Yazdi, and Ahad Harati. "Constrained Semi-Supervised Growing Self-Organizing Map." Neurocomputing 147 (January 2015): 456–71. http://dx.doi.org/10.1016/j.neucom.2014.06.039.

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Mehrizi, Ali, Hadi Sadoghi Yazdi, and Amir Hossein Taherinia. "Robust Semi-Supervised Growing Self-Organizing Map." Expert Systems with Applications 105 (September 2018): 23–33. http://dx.doi.org/10.1016/j.eswa.2018.03.046.

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Villmann, Th, and H. U. Bauer. "Applications of the growing self-organizing map." Neurocomputing 21, no. 1-3 (1998): 91–100. http://dx.doi.org/10.1016/s0925-2312(98)00037-x.

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Pramanik, Anima, Sobhan Sarkar, J. Maiti, and Pabitra Mitra. "RT-GSOM: Rough tolerance growing self-organizing map." Information Sciences 566 (August 2021): 19–37. http://dx.doi.org/10.1016/j.ins.2021.01.039.

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Murakoshi, Kazushi, and Satoshi Fujikawa. "Growing Hierarchical Self-Organizing Map Using Category Utility." International Journal of Software Engineering and Knowledge Engineering 26, no. 02 (2016): 217–37. http://dx.doi.org/10.1142/s0218194016500108.

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In order to automatically obtain hierarchical knowledge representation from a certain data, an unsupervised learning method has been developed that overcomes two problems of the growing hierarchical self-organizing map (GHSOM) method, which uses the quantization error, the deviation of the input data, as evaluation measure of the growing maps: proper control of the growth process of each map is difficult due to the use of the quantization error and the clusters in the hierarchical structure may be excessively subdivided. This improved GHSOM method uses the category utility (CU), a measure used
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Liao, Guang Lan, Tie Lin Shi, and Zi Rong Tang. "Gearbox Failure Detection Using Growing Hierarchical Self-Organizing Map." Key Engineering Materials 348-349 (September 2007): 177–80. http://dx.doi.org/10.4028/www.scientific.net/kem.348-349.177.

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Machine fault diagnosis is essentially an issue of pattern recognition, which heavily depends on suitable unsupervised learning method. The Self-Organizing Map (SOM), a popular unsupervised neural network, has been used for failure detection but with two limitations: needing predefined static architecture and lacking ability for the representation of hierarchical relations in the data. This paper presents a novel study on failure detection of gearbox using the Growing Hierarchical Self-Organizing Map (GHSOM), an artificial neural network model with hierarchical architecture composed of indepen
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Li, Baozhong, Yanming Liu, and Hailin Li. "Position Estimation Based on Grid Cells and Self-Growing Self-Organizing Map." Computational Intelligence and Neuroscience 2019 (February 26, 2019): 1–10. http://dx.doi.org/10.1155/2019/3606397.

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As the basis of animals’ natal homing behavior, path integration can continuously provide current position information relative to the initial position. Some neurons in freely moving animals’ brains can encode current positions and surrounding environments by special firing patterns. Research studies show that neurons such as grid cells (GCs) in the hippocampus of animals’ brains are related to the path integration. They might encode the coordinate of the animal’s current position in the same way as the residue number system (RNS) which is based on the Chinese remainder theorem (CRT). Hence, i
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Chattopadhyay, Manojit, Pranab K. Dan, and Sitanath Mazumdar. "Comparison of visualization of optimal clustering using self-organizing map and growing hierarchical self-organizing map in cellular manufacturing system." Applied Soft Computing 22 (September 2014): 528–43. http://dx.doi.org/10.1016/j.asoc.2014.04.027.

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GUO, XIAOLIAN, HAIYING WANG, and DAVID H. GLASS. "BAYESIAN SELF-ORGANIZING MAP FOR DATA CLASSIFICATION AND CLUSTERING." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 05 (2013): 1350037. http://dx.doi.org/10.1142/s0219691313500379.

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The Bayesian self-organizing map (BSOM) has typically been used for density estimation. In this study, we implemented an adaptation of the model for performing unsupervized and supervised classification. In order to determine the optimal number of neurons to represent the given dataset during the learning process, an extended Bayesian learning process is proposed called the growing BSOM. It starts with two neurons and adds new neurons to the network via a process in which the neuron with the lowest individual log-likelihood is identified. The system has been tested using three synthetic datase
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Tomi, Yahya Christyawan, Afif Supianto Ahmad, and Firdaus Mahmudy Wayan. "Anomaly-based intrusion detector system using restricted growing self organizing map." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (2019): 919–26. https://doi.org/10.11591/ijeecs.v13.i3.pp919-926.

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The rapid development of internet and network technology followed by malicious threats and attacks on networks and computers. Intrusion detection system (IDS) was developed to solve that problems. The development of IDS using machine learning is needed for classifying the attacks. One method of the classification is Self-Organizing Map (SOM). SOM able to perform classification and visualization in learning process to gain new knowledge. However, the SOM has less efficient in learning process when applied in Big Data. This study proposes Restricted Growing SOM method with clustering reference v
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Dissertations / Theses on the topic "Growing Self Organizing Map"

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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 adap
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Orts-Escolano, Sergio. "A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs." Doctoral thesis, Universidad de Alicante, 2013. http://hdl.handle.net/10045/36484.

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The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-O
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Liu, Yonggang. "Patterns and dynamics of ocean circulation variability on the West Florida shelf." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001413.

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Farshad, Tabrizi Seyed Ramin. "The Probabilistic Supervised Self-Organizing Map, PSSOM." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ31828.pdf.

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Schwardt, Martin. "Lösung ausgewählter Routenplanungsprobleme mit Hilfe der self-organizing map." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=975255126.

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Pourkia, Javid. "A SELF-ORGANIZING MAP APPROACH FOR HOSPITAL DATA ANALYSIS." OpenSIUC, 2014. https://opensiuc.lib.siu.edu/theses/1553.

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In this work, we utilize Self Organized Maps (SOM) to cluster and classify hospital related data with large dimensions, provided by Medicare website. These data have published every year and it includes numerous measures for each hospital in the nationwide. It might be possible to unearth some correlations in health-care industry by being able to interpreting this dataset, for example by examining the relations between data of immunizations department to readmission records and hospital expenses. It is not feasible to make any sense from these measures altogether using traditional methods (2D
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Choe, Yoonsuck. "Perceptual grouping in a self-organizing map of spiking neurons." Access restricted to users with UT Austin EID Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3025202.

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Wang, Dali. "Adaptive Double Self-Organizing Map for Clustering Gene Expression Data." Fogler Library, University of Maine, 2003. http://www.library.umaine.edu/theses/pdf/WangD2003.pdf.

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Bui, Michael. "Path finding on a spherical self-organizing map using distance transformations." Thesis, The University of Sydney, 2008. http://hdl.handle.net/2123/9290.

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Spatialization methods create visualizations that allow users to analyze high-dimensional data in an intuitive manner and facilitates the extraction of meaningful information. Just as geographic maps are simpli ed representations of geographic spaces, these visualizations are esssentially maps of abstract data spaces that are created through dimensionality reduction. While we are familiar with geographic maps for path planning/ nding applications, research into using maps of high-dimensional spaces for such purposes has been largely ignored. However, literature has shown that it is possible t
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Tervonen, J. (Jaakko). "Exploring behaviour patterns with self-organizing map for personalised mental stress detection." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201904131491.

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Abstract. Stress is an important health problem and the cause for many illnesses and working days lost. It is often measured with different questionnaires that capture only the current stress levels and may come in too late for early prevention. They are also prone to subjective inaccuracies since the feeling of stress, and the physiological response to it, have been found to be individual. Real-time stress detectors, trained on biosignals like heart rate variability, exist but majority of them employ supervised learning which requires collecting a large amount of labelled data from each syste
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Books on the topic "Growing Self Organizing Map"

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Klaus, Obermayer, and Sejnowski Terrence J, eds. Self-organizing map formation: Foundations of neural computation. MIT Press, 2001.

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Sahai, A. K. An objective study of Indian summer monsoon variability using the self organizing map algorithms. Indian Institute of Tropical Meteorology, 2006.

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United States. National Aeronautics and Space Administration., ed. Control of the NASA Langley 16-foot transonic tunnel with the self-organizing feature map. National Aeronautics and Space Administration, 1998.

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United States. National Aeronautics and Space Administration., ed. Control of the NASA Langley 16-foot transonic tunnel with the self-organizing feature map. National Aeronautics and Space Administration, 1998.

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United States. National Aeronautics and Space Administration., ed. Control of the NASA Langley 16-foot transonic tunnel with the self-organizing feature map. National Aeronautics and Space Administration, 1998.

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United States. National Aeronautics and Space Administration., ed. CONTROL OF THE NASA LANGLY 16-FOOT TRANSONIC TUNNEL WITH THE SELF-ORGANIZING FEATURE MAP... NASA/TM-98-206722... FEB. 25, 1998. s.n., 1999.

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Self Organizing Financial Stability Map. GRIN Verlag GmbH, 2013.

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Osherove, Roy. Elastic Leadership: Growing self-organizing teams. Manning Publications, 2016.

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Osherove, Roy. Elastic Leadership: Growing Self-Organizing Teams. Manning Publications Co. LLC, 2016.

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(Editor), Klaus Obermayer, and Terrence J. Sejnowski (Editor), eds. Self-Organizing Map Formation: Foundations of Neural Computation (Computational Neuroscience). The MIT Press, 2001.

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Book chapters on the topic "Growing Self Organizing Map"

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Moreno, Sebastián, Héctor Allende, Cristian Rogel, and Rodrigo Salas. "Robust Growing Hierarchical Self Organizing Map." In Computational Intelligence and Bioinspired Systems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494669_42.

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Smith, Toby, and Damminda Alahakoon. "Growing Self-Organizing Map for Online Continuous Clustering." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01088-0_3.

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Zhang, Stones Lei, Zhang Yi, and Jian Cheng Lv. "Growing Hierarchical Principal Components Analysis Self-Organizing Map." In Advances in Neural Networks - ISNN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759966_103.

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Matharage, Sumith, Damminda Alahakoon, Jayantha Rajapakse, and Pin Huang. "Fast Growing Self Organizing Map for Text Clustering." In Neural Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24958-7_48.

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Dittenbach, Michael, Andreas Rauber, and Dieter Merkl. "Recent Advances with the Growing Hierarchical Self-Organizing Map." In Advances in Self-Organising Maps. Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0715-6_20.

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Buczek, Bartłomiej M., and Paweł B. Myszkowski. "Growing Hierarchical Self-Organizing Map for Images Hierarchical Clustering." In Computational Collective Intelligence. Technologies and Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23935-9_5.

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Liao, Guang Lan, Tie Lin Shi, and Zi Rong Tang. "Gearbox Failure Detection Using Growing Hierarchical Self-Organizing Map." In Advances in Fracture and Damage Mechanics VI. Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/0-87849-448-0.177.

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Andreakis, Andreas, Nicolai v. Hoyningen-Huene, and Michael Beetz. "Incremental Unsupervised Time Series Analysis Using Merge Growing Neural Gas." In Advances in Self-Organizing Maps. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02397-2_2.

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Tokunaga, Kazuhiro. "Growing Graph Network Based on an Online Gaussian Mixture Model." In Advances in Self-Organizing Maps. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21566-7_11.

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do Rego, Renata L. M. E., Hansenclever F. Bassani, Daniel Filgueiras, and Aluizio F. R. Araujo. "Surface Reconstruction Method Based on a Growing Self-Organizing Map." In Artificial Neural Networks – ICANN 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04274-4_54.

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Conference papers on the topic "Growing Self Organizing Map"

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Salgado, Paulo, Teresa Perdicoullis, P. Lopes dos Santos, and Paulo A. F. N. A. Afonso. "Hierarchical Self-Organizing Map as Nonlinear Classificator." In 2024 IEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2024. https://doi.org/10.1109/cinti63048.2024.10830843.

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Yeloglu, Ozge, A. Nur Zincir-Heywood, and Malcolm I. Heywood. "Growing recurrent self organizing map." In 2007 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icsmc.2007.4414001.

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DalleMole, Vilson L., and Aluizio F. R. Araujo. "The growing Self-organizing surface Map." In 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008 - Hong Kong). IEEE, 2008. http://dx.doi.org/10.1109/ijcnn.2008.4634081.

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Dittenbach, M., D. Merkl, and A. Rauber. "The growing hierarchical self-organizing map." In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.859366.

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Tai, Wei-Shen, and Chung-Chian Hsu. "A growing mixed Self-Organizing Map." In 2010 Sixth International Conference on Natural Computation (ICNC). IEEE, 2010. http://dx.doi.org/10.1109/icnc.2010.5582894.

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Yu, Yaohua, and Damminda Alahakoon. "Batch implementation of Growing Self-Organizing Map." In 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06). IEEE, 2006. http://dx.doi.org/10.1109/cimca.2006.58.

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DalleMole, Vilson L., and Aluizio F. R. Araújo. "The Growing Self-Organizing Surface Map: Improvements." In 2008 10th Brazilian Symposium on Neural Networks (SBRN). IEEE, 2008. http://dx.doi.org/10.1109/sbrn.2008.16.

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Overbeek, Marlinda Vasty, Wisnu Ananta Kusuma, and Agus Buono. "Clustering metagenome fragments using growing self organizing map." In 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE, 2013. http://dx.doi.org/10.1109/icacsis.2013.6761590.

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Cao, Mengxue, Aijun Li, Qiang Fang, and Bernd J. Kroger. "Growing self-organizing map approach for semantic acquisition modeling." In 2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom). IEEE, 2013. http://dx.doi.org/10.1109/coginfocom.2013.6719269.

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Guo, Xiao-Lian, Hai-Ying Wang, and David H. Glass. "A growing Bayesian self-organizing map for data clustering." In 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6359011.

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Reports on the topic "Growing Self Organizing Map"

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Ortiz, M. Growing Self-Organizing Maps as Predictors for Photometric Redshift. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1557954.

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Eguchi, Junji, and Manabu Murakami. Application of Self-Organizing Map to Inspection Technology for Gear Surface. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0575.

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Herrera, Allen, and Alexander Heifetz. Detection of Anomalies in Gamma Background Radiation Data with K-Means and Self-Organizing Map Clustering Algorithms - Consortium on Nuclear Security Technologies (CONNECT) Q1 Report. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1841591.

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