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Journal articles on the topic 'Self-organizing map'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Rougier, Nicolas P., and Georgios Is Detorakis. "Randomized Self-Organizing Map." Neural Computation 33, no. 8 (2021): 2241–73. http://dx.doi.org/10.1162/neco_a_01406.

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We propose a variation of the self-organizing map algorithm by considering the random placement of neurons on a two-dimensional manifold, following a blue noise distribution from which various topologies can be derived. These topologies possess random (but controllable) discontinuities that allow for a more flexible self-organization, especially with high-dimensional data. The proposed algorithm is tested on one-, two- and three-dimensional tasks, as well as on the MNIST handwritten digits data set and validated using spectral analysis and topological data analysis tools. We also demonstrate t

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2

Tatoian, Robert, and Lutz Hamel. "Self-Organizing Map Convergence." International Journal of Service Science, Management, Engineering, and Technology 9, no. 2 (2018): 61–84. http://dx.doi.org/10.4018/ijssmet.2018040103.

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Self-organizing maps are artificial neural networks designed for unsupervised machine learning. Here in this article, the authors introduce a new quality measure called the convergence index. The convergence index is a linear combination of map embedding accuracy and estimated topographic accuracy and since it reports a single statistically meaningful number it is perhaps more intuitive to use than other quality measures. The convergence index in the context of clustering problems was proposed by Ultsch as part of his fundamental clustering problem suite as well as real world datasets. First d

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3

Kohonen, T. "The self-organizing map." Proceedings of the IEEE 78, no. 9 (1990): 1464–80. http://dx.doi.org/10.1109/5.58325.

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4

Vuorimaa, Petri. "Fuzzy self-organizing map." Fuzzy Sets and Systems 66, no. 2 (1994): 223–31. http://dx.doi.org/10.1016/0165-0114(94)90312-3.

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5

Kohonen, Teuvo. "The self-organizing map." Neurocomputing 21, no. 1-3 (1998): 1–6. http://dx.doi.org/10.1016/s0925-2312(98)00030-7.

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6

Pal, Sankar K., Biswarup Dasgupta, and Pabitra Mitra. "Rough Self Organizing Map." Applied Intelligence 21, no. 3 (2004): 289–99. http://dx.doi.org/10.1023/b:apin.0000043561.99513.69.

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7

Saini, Naveen, Sriparna Saha, Sahil Mansoori, and Pushpak Bhattacharyya. "Fusion of self-organizing map and granular self-organizing map for microblog summarization." Soft Computing 24, no. 24 (2020): 18699–711. http://dx.doi.org/10.1007/s00500-020-05104-2.

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8

Sampath, R., and A. Saradha. "Alzheimer’s Disease Image Segmentation with Self-Organizing Map Network." Journal of Software 10, no. 6 (2015): 670–80. http://dx.doi.org/10.17706//jsw.10.6.67-680.

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9

Sampath, R., and A. Saradha. "Alzheimer’s Disease Image Segmentation with Self-Organizing Map Network." Journal of Software 10, no. 6 (2015): 670–78. http://dx.doi.org/10.17706//jsw.10.6.670-680.

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10

FURUKAWA, Tetsuo. "Self-organizing map of a set of self-organizing maps." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 19, no. 6 (2007): 618–26. http://dx.doi.org/10.3156/jsoft.19.6_618.

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11

Yasunaga, Moritoshi. "Hardware Implementation of Self-Organizing Map." Journal of The Japan Institute of Electronics Packaging 23, no. 2 (2020): 128–34. http://dx.doi.org/10.5104/jiep.23.128.

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12

Kuo, Huang-Cheng, and Shih-Hao Chen. "Self-Organizing Map Learning with Momentum." Computer and Information Science 9, no. 1 (2016): 136. http://dx.doi.org/10.5539/cis.v9n1p136.

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<p class="zhengwen"><span lang="EN-GB">Self-organizing map (SOM) is a type of artificial neural network for cluster analysis. Each neuron in the map competes with others for the input data objects in order to learn the grouping of the input space. Besides competition, neighbor neurons of a winning neuron also learn. SOM has a natural propensity to cluster data into visually distinct clusters, which show the intrinsic grouping of data.</span></p><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family:

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13

Vesanto, J., and E. Alhoniemi. "Clustering of the self-organizing map." IEEE Transactions on Neural Networks 11, no. 3 (2000): 586–600. http://dx.doi.org/10.1109/72.846731.

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14

Mu-Chun Su and Hsiao-Te Chang. "Fast self-organizing feature map algorithm." IEEE Transactions on Neural Networks 11, no. 3 (2000): 721–33. http://dx.doi.org/10.1109/72.846743.

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15

Kittiwachana, Sila, Diana L. S. Ferreira, Louise A. Fido, Duncan R. Thompson, Richard E. A. Escott, and Richard G. Brereton. "Self-Organizing Map Quality Control Index." Analytical Chemistry 82, no. 14 (2010): 5972–82. http://dx.doi.org/10.1021/ac100383g.

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16

Kohonen, Teuvo. "Essentials of the self-organizing map." Neural Networks 37 (January 2013): 52–65. http://dx.doi.org/10.1016/j.neunet.2012.09.018.

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17

Ferles, Christos, Yannis Papanikolaou, and Kevin J. Naidoo. "Denoising Autoencoder Self-Organizing Map (DASOM)." Neural Networks 105 (September 2018): 112–31. http://dx.doi.org/10.1016/j.neunet.2018.04.016.

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18

Yang, Miin-Shen, Wen-Liang Hung, and De-Hua Chen. "Self-organizing map for symbolic data." Fuzzy Sets and Systems 203 (September 2012): 49–73. http://dx.doi.org/10.1016/j.fss.2012.04.006.

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19

Berglund, E., and J. Sitte. "The Parameterless Self-Organizing Map Algorithm." IEEE Transactions on Neural Networks 17, no. 2 (2006): 305–16. http://dx.doi.org/10.1109/tnn.2006.871720.

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20

Cheung, Yiu-ming, and Lap-tak Law. "Rival-Model Penalized Self-Organizing Map." IEEE Transactions on Neural Networks 18, no. 1 (2007): 289–95. http://dx.doi.org/10.1109/tnn.2006.885039.

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21

MASUDA, Masato, Yasushi NAKABAYASHI, and Genki YAGAWA. "Radius Parallel Self-Organizing Map (RPSOM)." Journal of Computational Science and Technology 6, no. 1 (2012): 16–27. http://dx.doi.org/10.1299/jcst.6.16.

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22

TAMURA, Yoshiaki, and Masato MASUDA. "Flow Visualization Using Self-Organizing Map." Proceedings of The Computational Mechanics Conference 2022.35 (2022): 16–17. http://dx.doi.org/10.1299/jsmecmd.2022.35.16-17.

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23

Özçift, Akın, Mehmet Kaya, Arif Gülten, and Mustafa Karabulut. "Swarm optimized organizing map (SWOM): A swarm intelligence basedoptimization of self-organizing map." Expert Systems with Applications 36, no. 7 (2009): 10640–48. http://dx.doi.org/10.1016/j.eswa.2009.02.051.

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24

Aoki, Takaaki, and Toshio Aoyagi. "Self-Organizing Maps with Asymmetric Neighborhood Function." Neural Computation 19, no. 9 (2007): 2515–35. http://dx.doi.org/10.1162/neco.2007.19.9.2515.

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The self-organizing map (SOM) is an unsupervised learning method as well as a type of nonlinear principal component analysis that forms a topologically ordered mapping from the high-dimensional data space to a low-dimensional representation space. It has recently found wide applications in such areas as visualization, classification, and mining of various data. However, when the data sets to be processed are very large, a copious amount of time is often required to train the map, which seems to restrict the range of putative applications. One of the major culprits for this slow ordering time i

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25

Han, Soo-Whan. "Self-Organizing Map for Blind Channel Equalization." Journal of information and communication convergence engineering 8, no. 6 (2010): 609–17. http://dx.doi.org/10.6109/jicce.2010.8.6.609.

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26

ZHENG, Hui-Cheng, and Wei SHEN. "A Localized Linear Manifold Self-organizing Map." Acta Automatica Sinica 34, no. 10 (2009): 1298–304. http://dx.doi.org/10.3724/sp.j.1004.2008.01298.

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27

Sakkari, Mohamed, Monia Hamdi, Hela Elmannai, Abeer AlGarni, and Mourad Zaied. "Feature Extraction-Based Deep Self-Organizing Map." Circuits, Systems, and Signal Processing 41, no. 5 (2022): 2802–24. http://dx.doi.org/10.1007/s00034-021-01914-3.

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28

Żochowski, Michał, and Larry S. Liebovitch. "Self-organizing dynamics of coupled map systems." Physical Review E 59, no. 3 (1999): 2830–37. http://dx.doi.org/10.1103/physreve.59.2830.

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29

TOKUNAGA, Kazuhiro. "Modular Network for Generalized Self-Organizing Map." IEICE ESS Fundamentals Review 14, no. 2 (2020): 97–106. http://dx.doi.org/10.1587/essfr.14.2_97.

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30

Primandari, Arum Handini, and Nur Aini Ikasakti. "Job applicants clustering using self-organizing map." Bulletin of Social Informatics Theory and Application 1, no. 2 (2017): 60–71. http://dx.doi.org/10.31763/businta.v1i2.28.

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    Yogyakarta Government through Directorate of Manpower and Transmigration (Disnakertrans) have been canvassing people looking for job. An employment program was provided by Disnakertrans to allow job applicants meet companies. This research was carried out to identify educational background of applicants, in order to obtain the suitable worker. One of the ways to identify educational background is by district clustering in Yogyakarta. Clustering method is employed to reveal the characteristic of educational quality in every district in Yogyakarta. Clustering is a gro

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31

NISHIYAMA, Koji, Shinichi ENDO, and Kenji JINNO. "RAINFALL PREDICTION BASED ON SELF-ORGANIZING MAP." PROCEEDINGS OF HYDRAULIC ENGINEERING 50 (2006): 403–8. http://dx.doi.org/10.2208/prohe.50.403.

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32

Similä, Timo. "Self-Organizing Map Learning Nonlinearly Embedded Manifolds." Information Visualization 4, no. 1 (2005): 22–31. http://dx.doi.org/10.1057/palgrave.ivs.9500088.

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One of the main tasks in exploratory data analysis is to create an appropriate representation for complex data. In this paper, the problem of creating a representation for observations lying on a low-dimensional manifold embedded in high-dimensional coordinates is considered. We propose a modification of the Self-organizing map (SOM) algorithm that is able to learn the manifold structure in the high-dimensional observation coordinates. Any manifold learning algorithm may be incorporated to the proposed training strategy to guide the map onto the manifold surface instead of becoming trapped in

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33

Kohonen, T., E. Oja, O. Simula, A. Visa, and J. Kangas. "Engineering applications of the self-organizing map." Proceedings of the IEEE 84, no. 10 (1996): 1358–84. http://dx.doi.org/10.1109/5.537105.

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34

Obayashi, Shigeru. "Design Datamining Based on Self-Organizing Map." Proceedings of the JSME annual meeting 2002.7 (2002): 23–24. http://dx.doi.org/10.1299/jsmemecjo.2002.7.0_23.

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35

Gunawan, D., D. Arisandi, F. M. Ginting, R. F. Rahmat, and A. Amalia. "Russian Character Recognition using Self-Organizing Map." Journal of Physics: Conference Series 801 (January 2017): 012040. http://dx.doi.org/10.1088/1742-6596/801/1/012040.

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36

Velzen, G. A. van. "Instabilities in Kohonen's self-organizing feature map." Journal of Physics A: Mathematical and General 27, no. 5 (1994): 1665–81. http://dx.doi.org/10.1088/0305-4470/27/5/029.

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37

López-Rubio, Ezequiel, José Muñoz-Pérez, and José Antonio Gómez-Ruiz. "A principal components analysis self-organizing map." Neural Networks 17, no. 2 (2004): 261–70. http://dx.doi.org/10.1016/j.neunet.2003.04.001.

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38

Rynkiewicz, Joseph. "Self-organizing map algorithm and distortion measure." Neural Networks 19, no. 6-7 (2006): 830–37. http://dx.doi.org/10.1016/j.neunet.2006.05.016.

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39

Ferles, Christos, and Andreas Stafylopatis. "Self-Organizing Hidden Markov Model Map (SOHMMM)." Neural Networks 48 (December 2013): 133–47. http://dx.doi.org/10.1016/j.neunet.2013.07.011.

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40

López-Rubio, Ezequiel, and Antonio Díaz Ramos. "Grid topologies for the self-organizing map." Neural Networks 56 (August 2014): 35–48. http://dx.doi.org/10.1016/j.neunet.2014.05.001.

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41

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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42

Shah-Hosseini, Hamed. "Binary tree time adaptive self-organizing map." Neurocomputing 74, no. 11 (2011): 1823–39. http://dx.doi.org/10.1016/j.neucom.2010.07.037.

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43

Sarlin, Peter, and Zhiyuan Yao. "Clustering of the Self-Organizing Time Map." Neurocomputing 121 (December 2013): 317–27. http://dx.doi.org/10.1016/j.neucom.2013.04.007.

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44

Yang, Zheng Rong, and Kuo-Chen Chou. "Mining Biological Data Using Self-Organizing Map." Journal of Chemical Information and Computer Sciences 43, no. 6 (2003): 1748–53. http://dx.doi.org/10.1021/ci034138n.

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45

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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46

Ruz, Gonzalo A., and Duc Truong Pham. "NBSOM: The naive Bayes self-organizing map." Neural Computing and Applications 21, no. 6 (2011): 1319–30. http://dx.doi.org/10.1007/s00521-011-0567-9.

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47

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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48

Kiviluoto, Kimmo. "Predicting bankruptcies with the self-organizing map." Neurocomputing 21, no. 1-3 (1998): 191–201. http://dx.doi.org/10.1016/s0925-2312(98)00038-1.

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49

Hsu, C. C. "Generalizing Self-Organizing Map for Categorical Data." IEEE Transactions on Neural Networks 17, no. 2 (2006): 294–304. http://dx.doi.org/10.1109/tnn.2005.863415.

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50

Hovakimyan, Anna Sedrak, Siranush Gegham Sargsyan, and Arshak Nazaryan. "Self-Organizing Map Application for Iris Recognition." Journal of Communications and Computer Engineering 3, no. 2 (2014): 10. http://dx.doi.org/10.20454/jcce.2013.760.

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Human iris is a good subject of biometrical identification, since iris patterns are unique like fingerprints. Iris is well protected against damage, unlike fingerprints, which can be harder to recognize after years of certain types of manual labor.A problem of iris recognition is considered in the paper. In machine learning, pattern recognition is the assignment of a label to a given input value. Pattern classification is an example of pattern recognition: it attempts to assign each input value to one of a given set of classes. Nowadays various techniques are used for this purpose, and in part

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