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Enhancing Visual Clustering Using Adaptive Moving Self-Organizing Maps (AMSOM)

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Agents and Artificial Intelligence (ICAART 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10162))

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Abstract

Recent advancements in computing technology allowed both scientific and business applications to produce large datasets with increasing complexity and dimensionality. Clustering algorithms are useful in analyzing these large datasets but often fall short to provide completely satisfactory results. Integrating clustering and visualization not only yields better clustering results but also leads to a higher degree of confidence in the findings. Self-Organizing Map (SOM) is a neural network model which is used to obtain a topology-preserving mapping from the (usually high dimensional) input/feature space to an output/map space of fewer dimensions (usually two or three in order to facilitate visualization). Neurons in the output space are connected with each other but this structure remains fixed throughout training and learning is achieved through the updating of neuron reference vectors in feature space. Despite the fact that growing variants of SOM overcome the fixed structure limitation, they increase computational cost and also do not allow the removal of a neuron after its introduction. In this paper, a variant of SOM is presented called AMSOM (Adaptive Moving Self-Organizing Map) that on the one hand creates a more flexible structure where neuron positions are dynamically altered during training and on the other hand tackles the drawback of having a predefined grid by allowing neuron addition and/or removal during training. Experimental evaluation on different literature datasets with diverse characteristics improves SOM training performance, leads to a better visualization of the input dataset, and provides a framework for determining the optimal number and structure of neurons as well as the optimal number of clusters.

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Notes

  1. 1.

    http://archive.ics.uci.edu/ml/.

  2. 2.

    http://www.cad.zju.edu.cn/home/dengcai/Data/TextData.html.

References

  1. Alahakoon, D., Halgamuge, S.K., Srinivasan, B.: Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Trans. Neural Netw. 11(3), 601–614 (2000)

    Article  Google Scholar 

  2. Andrienko, G., Andrienko, N., Rinzivillo, S., Nanni, M., Pedreschi, D., Giannotti, F.: Interactive visual clustering of large collections of trajectories. In: IEEE Symposium on Visual Analytics Science and Technology, VAST, pp. 3–10. IEEE (2009)

    Google Scholar 

  3. Ayadi, T., Hamdani, T.M., Alimi, A.M.: MIGSOM: multilevel interior growing self-organizing maps for high dimensional data clustering. Neural Process. Lett. 36(3), 235–256 (2012)

    Article  Google Scholar 

  4. Bauer, H.-U., Herrmann, M., Villmann, T.: Neural maps and topographic vector quantization. Neural Netw. 12(4), 659–676 (1999)

    Article  Google Scholar 

  5. Blackmore, J., Miikkulainen, R.: Incremental grid growing: encoding high-dimensional structure into a two-dimensional feature map. In: IEEE International Conference on Neural Network, pp. 450–455 (1993)

    Google Scholar 

  6. Bortman, M., Aladjem, M.: A growing and pruning method for radial basis function networks. IEEE Trans. Neural Netw. 20(6), 1039–1045 (2009)

    Article  Google Scholar 

  7. Brugger, D., Bogdan, M., Rosenstiel, W.: Automatic cluster detection in Kohonen’s SOM. IEEE Trans. Neural Netw. 19(3), 442–459 (2008)

    Article  Google Scholar 

  8. Bruneau, P., Otjacques, B.: An interactive, example-based, visual clustering system. In: 17th International Conference on Information Visualisation, pp. 168–173. IEEE (2013)

    Google Scholar 

  9. Ceccarelli, M., Petrosino, A., Vaccaro, R.: Competitive neural networks on message-passing parallel computers. Concurrency Pract. Exp. 5(6), 449–470 (1993)

    Article  Google Scholar 

  10. Changeux, J.P., Danchin, A.: Selective stabilisation of developing synapses as a mechanism for the specification of neuronal networks. Nature 264(5588), 705–712 (1976)

    Article  Google Scholar 

  11. Chaudhary, V., Bhatia, R.S., Ahlawat, A.K.: An efficient self-organizing map (E-SOM) learning algorithm using group of neurons. Int. J. Comput. Intell. Syst. 7(5), 963–972 (2014)

    Article  Google Scholar 

  12. Deboeck, G., Kohonen, T.: Visual Explorations in Finance: With Self-Organizing Maps. Springer Science & Business Media, Heidelberg (2013)

    MATH  Google Scholar 

  13. Doherty, K., Adams, R., Davey, N.: TreeGNG-hierarchical topological clustering. In: ESANN, pp. 19–24 (2005)

    Google Scholar 

  14. Dowling, J.E.: Debate, The Great Brain : Nature or Nurture?. Princeton University Press, Princeton (2007)

    Google Scholar 

  15. Estévez, P.A., Príncipe, J.C., Zegers, P.: Advances in Self-Organizing Maps: 9th International Workshop, WSOM Santiago, Chile, December 12–14, Proceedings. Springer Science & Business Media (2012)

    Google Scholar 

  16. Fort, J.-C.: SOMS mathematics. Neural Netw. 19(6), 812–816 (2006)

    Article  MATH  Google Scholar 

  17. Fritzke, B.: Growing cell structuresa self-organizing network for unsupervised and supervised learning. Neural Netw. 7(9), 1441–1460 (1994)

    Article  Google Scholar 

  18. Fritzke, B.: Growing grida self-organizing network with constant neighborhood range and adaptation strength. Neural Process. Lett. 2(5), 9–13 (1995)

    Article  Google Scholar 

  19. Fritzke, B., et al.: A growing neural gas network learns topologies. Adv. Neural Inf. Proc. Syst. 7, 625–632 (1995)

    Google Scholar 

  20. Halkidi, M., Vazirgiannis, M.: A density-based cluster validity approach using multi-representatives. Pattern Recogn. Lett. 29(6), 773–786 (2008)

    Article  Google Scholar 

  21. Han, H.-G., Qiao, J.-F.: A structure optimisation algorithm for feedforward neural network construction. Neurocomputing 99, 347–357 (2013)

    Article  Google Scholar 

  22. Hodge, V.J., Austin, J.: Hierarchical growing cell structures: TreeGCS. IEEE Trans. Knowl. Data Eng. 13(2), 207–218 (2001)

    Article  Google Scholar 

  23. Islam, M., Sattar, A., Amin, F., Yao, X., Murase, K.: A new adaptive merging and growing algorithm for designing artificial neural networks. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 39(3), 705–722 (2009)

    Article  Google Scholar 

  24. Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43(1), 59–69 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  25. Kohonen, T.: The ‘neural’ phonetic typewriter. Computer 21(3), 11–22 (1988)

    Article  Google Scholar 

  26. Kohonen, T.: Things you haven’t heard about the self-organizing map. In: IEEE International Conference on Neural Networks, pp. 1147–1156. IEEE (1993)

    Google Scholar 

  27. Teuvo, K.: Self-organizing Maps. Springer Series in Information Sciences. Springer, Berlin (2001)

    MATH  Google Scholar 

  28. Kohonen, T.: Self-organization and Associative Memory, vol. 8. Springer, Heidelberg (2012)

    MATH  Google Scholar 

  29. Krista, L., Timo, H., Samuel, K., Teuvo, K.: WEBSOM for textual data mining. Artif. Intell. Rev. 13(5–6), 345–364 (1999)

    Google Scholar 

  30. Li, Z., Eastman, J.R.: The nature and classification of unlabelled neurons in the use of Kohonen’s self organizing map for supervised classification. Trans. GIS 10(4), 599–613 (2006)

    Article  Google Scholar 

  31. Lu, S.Y.: Pattern classification using self-organizing feature maps. In: IJCNN International Joint Conference on 1990, pp. 471–480 (1990)

    Google Scholar 

  32. Maiorana, F., Mastorakis, NE., Poulos, M., Mladenov, V., Bojkovic, Z., Simian, D., Kartalopoulos, S., Varonides, A., Udriste, C.: Performance improvements of a Kohonen self organizing classification algorithm on sparse data sets. In: Proceedings of WSEAS International Conference on Mathematics and Computers in Science and Engineering, vol. 10. WSEAS (2008)

    Google Scholar 

  33. Marsland, S., Shapiro, J., Nehmzow, U.: A self-organising network that grows when required. Neural Netw. 15(8), 1041–1058 (2002)

    Article  Google Scholar 

  34. Mulier, F., Cherkassky, V.: Learning rate schedules for self-organizing maps. In: Proceedings of the 12th IAPR International. Conference on Pattern Recognition, vol. 2-Conference B: Computer Vision and Image Processing, vol. 2, pp. 224–228. IEEE (1994)

    Google Scholar 

  35. Narasimha, P.L., Delashmit, W.H., Manry, M.T., Li, J., Maldonado, F.: An integrated growing-pruning method for feedforward network training. Neurocomputing 71(13), 2831–2847 (2008)

    Article  Google Scholar 

  36. Odri, S.V., Petrovacki, D.P., Krstonosic, G.A.: Evolutional development of a multilevel neural network. Neural Netw. 6(4), 583–595 (1993)

    Article  Google Scholar 

  37. Park, Y.S., Tison, J., Lek, S., Giraudel, J.L., Coste, M., Delmas, F.: Application of a self-organizing map to select representative species in multivariate analysis: a case study determining diatom distribution patterns across France. Ecol. Inf. 1(3), 247–257 (2006). 4th International Conference on Ecological Informatics

    Article  Google Scholar 

  38. Phuc, D., Hung, MX.: Using SOM based graph clustering for extracting main ideas from documents. In: IEEE International Conference on Research, Innovation and Vision for the Future, RIVF, pp. 209–214. IEEE (2008)

    Google Scholar 

  39. Rauber, A., Merkl, D., Dittenbach, M.: The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data. IEEE Trans. Neural Netw. 13(6), 1331–1341 (2002)

    Article  MATH  Google Scholar 

  40. Spanakis, G., Siolas, G., Stafylopatis, A.: DoSO: a document self-organizer. J. Intell. Inf. Syst. 39(3), 577–610 (2012)

    Article  Google Scholar 

  41. Taşdemir, K.: Spectral clustering as an automated SOM segmentation tool. In: Laaksonen, J., Honkela, T. (eds.) WSOM 2011. LNCS, vol. 6731, pp. 71–78. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21566-7_7

    Chapter  Google Scholar 

  42. Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Trans. Neural Netw. 11(3), 586–600 (2000)

    Article  Google Scholar 

  43. Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM toolbox for Matlab 5. Citeseer (2000)

    Google Scholar 

  44. Wedeen, V.J., Rosene, D.L., Wang, R., Dai, G., Mortazavi, F., Hagmann, P., Kaas, J.H., Tseng, W.I.: The geometric structure of the brain fiber pathways. Science 335(6076), 1628–1634 (2012)

    Article  Google Scholar 

  45. Sitao, W., Chow, T.W.S.: Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density. Pattern Recogn. 37(2), 175–188 (2004)

    Article  MATH  Google Scholar 

  46. Yang, S.-H., Chen, Y.-P.: An evolutionary constructive and pruning algorithm for artificial neural networks and its prediction applications. Neurocomputing 86, 140–149 (2012)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Data Science and Knowledge Engineering, Maastricht University, 6200MD, Maastricht, Netherlands

    Gerasimos Spanakis & Gerhard Weiss

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  1. Gerasimos Spanakis
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  2. Gerhard Weiss
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Correspondence to Gerasimos Spanakis .

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Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

  2. Polytechnic Institute of Setúbal/INSTICC, Setúbal, Portugal

    Joaquim Filipe

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Spanakis, G., Weiss, G. (2017). Enhancing Visual Clustering Using Adaptive Moving Self-Organizing Maps (AMSOM). In: van den Herik, J., Filipe, J. (eds) Agents and Artificial Intelligence. ICAART 2016. Lecture Notes in Computer Science(), vol 10162. Springer, Cham. https://doi.org/10.1007/978-3-319-53354-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-53354-4_11

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