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MIGSOM: Multilevel Interior Growing Self-Organizing Maps for High Dimensional Data Clustering

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Abstract

Understanding the inherent structure of high-dimensional datasets is a very challenging task. This can be tackled from visualization, summarizing or simply clustering points of view. The Self-Organizing Map (SOM) is a powerful and unsupervised neural network to resolve these kinds of problems. By preserving the data topology mapped onto a grid, SOM can facilitate visualization of data structure. However, classical SOM still suffers from the limits of its predefined structure. Growing variants of SOM can overcome this problem, since they have tried to define a network structure with no need an advance a fixed number of output units by dynamic growing architecture. In this paper we propose a new dynamic SOMs called MIGSOM: Multilevel Interior Growing SOMs for high-dimensional data clustering. MIGSOM present a different architecture than dynamic variants presented in the literature. Using an unsupervised training process MIGSOM has the capability of growing map size from the boundaries as well as the interior of the network in order to represent more faithfully the structure present in a data collection. As a result, MIGSOM can have three-dimensional (3-D) structure with different levels of oriented maps developed according to data direction. We demonstrate the potential of the MIGSOM with real-world datasets of high-dimensional properties in terms of topology preserving visualization, vectors summarizing by efficient quantization and data clustering. In addition, MIGSOM achieves better performance compared to growing grid and the classical SOM.

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References

  1. Alahakoon LD, Halgamuge SK, Sirinivasan B (2000) Dynamic self organizing maps with controlled growth for knowledge discovery. IEEE Trans Neural Netw 11(3): 601–614

    Article  Google Scholar 

  2. Amarasiri R, Alahakoon D, Smith KA (2004) HDGSOM: a modified growing self-organizing map for high dimensional data clustering. In: Fourth international conference on hybrid intelligent systems (HIS’04), pp 216–221

  3. Amarasiri R, Alahakoon D, Smith K, Premaratne M (2005) HDGSOMr: a high dimensional growing self-organizing map using randomness for efficient Web and text mining. In: Proceedings of the 2005 IEEEWICACM international conference on web intelligence, pp 215–221

  4. Ambroise C, Sèze G, Badran F, Thiria S (2000) Hierarchical clustering of self-organizing maps for cloud classification. Neurocomputing 30(1): 47–52

    Article  Google Scholar 

  5. Ayadi T, Hamdani MT, Alimi MA, Khabou MA (2007) 2IBGSOM: interior and irregular boundaries growing self-organizing maps. In: IEEE sixth international conference on machine learning and applications, pp 387–392

  6. Ayadi T, Hamdani MT, Alimi MA (2010) A new data topology matching technique with multilevel interior growing self-organizing maps. In: IEEE international conference on systems, man, and cybernetics, pp 2479–2486

  7. Bauer H-U, Herrmann M, Villmann T (1999) Neural maps and topographic vector quantization. Neural Netw 12(4–5): 659–676

    Article  Google Scholar 

  8. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum, New York

    Book  MATH  Google Scholar 

  9. Blackmore J, Miikkulainen R (1993) Incremental grid growing: encoding high-dimensional structure into a two-dimensional feature map. In: Proceedings of the international conference on neural networks, vol 1, pp 450–455

  10. Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1(2): 224–227

    Article  Google Scholar 

  11. Deboeck GJ, Kohonen T (1998) Visual explorations in finance: with self-organising maps. Springer, Berlin

    Google Scholar 

  12. Delgado S, Gonzalo C, Martinez E, Arquero A (2009) Topology preserving visualization methods for growing self-organizing maps. In: IWANN 2009, Part I, LNCS, vol 5517, pp 196–203

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

    Article  Google Scholar 

  14. Doherty KAJ, Adams RG, Davey N (2005) TreeGNG—hierarchical topological clustering. In: ESANN: European symposium on artificial neural networks, pp 19–24

  15. Ellouze M, Boujemaa N, Alimi MA (2009) Scene pathfinder: unsupervised clustering techniques for movie scenes extraction. Multimed Tools Appl 47: 325–346

    Article  Google Scholar 

  16. Freeman RT, Yin H (2004) Adaptive topological tree structure for document organization and visualization. Neural Netw 17: 1255–1271

    Article  MATH  Google Scholar 

  17. Fritzke B (1994) Growing cell structure: a self organizing network for supervised and un-supervised learning. Neural Netw 7: 1441–1460

    Article  Google Scholar 

  18. Fritzke B (1995a) A growing neural gas network learns topologies. Adv Neural Inf Process Syst 7: 625–632

    Google Scholar 

  19. Fritzke B (1995b) Growing grid: a self-organizing network with constant neighbourhood range and adaption strength. Neural Process Lett 2(5): 1–5

    Article  Google Scholar 

  20. Halkidi M, Vazirgiannis M (2000) Quality scheme assessment in the clustering process. In: Proceedings of the PKDD (principles and practice of knowledge discovery in databases), Lyon, France. Lecture notes in artificial intelligence, vol 1910. Springer, GmbH, Berlin, pp 265–276

  21. Halkidi M, Vazirgiannis M (2002) Clustering validity assessment using multi representatives. In: 2nd Hellenic conference on AI, SETN-2, pp 237–248

  22. Hamdani MT, Alimi MA, Karray F (2008) Enhancing the structure and parameters of the centers for BBF fuzzy neural network classifier construction based on data structure. In: IEEE international joint conference on neural networks, Hong Kong, pp 3174–3180

  23. Hamdani MT, Khabou MA, Alimi MA (2010) Conflict negotiation process with stress parameters control for new classifier decision fusion scheme. In: International conference on IP, comp vision and pattern recognition, IPCV, pp 784–787

  24. Hamdani MT, Alimi MA, Khabou MA (2011) An iterative method for deciding svm and single layer neural network structures. Neural Process Lett 33(2): 171–186

    Article  Google Scholar 

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

    Article  Google Scholar 

  26. Kiang MY (2001) Extending the Kohonen self-organizing map networks for clustering analysis. Comput Stat Data Anal 38: 161–180

    Article  MathSciNet  MATH  Google Scholar 

  27. Kim M, Ramakrishna RS (2005) New indices for cluster validity assessment. Pattern Recognit Lett 26(15): 2353–2363

    Article  Google Scholar 

  28. Kohonen T (1982) Self organized formation of topological correct feature maps. Biol Cybern 43: 59–69

    Article  MathSciNet  MATH  Google Scholar 

  29. Kohonen T (1988) Statistical pattern recognition with neural networks: benchmark studies. IEEE Int Conf Neural Netw 1: 61–68

    Article  Google Scholar 

  30. Kohonen T (1989) Self-organization and associative memory, 3rd edn. Springer, Berlin

    Book  Google Scholar 

  31. Kohonen T (1998) The self-organizing map. Neurocomputing 21: 1–6

    Article  MATH  Google Scholar 

  32. Kohonen T, Schroeder MR, Huang TS (2001) Self organizing maps, 3rd edn. Springer, New York

    Book  MATH  Google Scholar 

  33. Lampinen J, Oja E (1992) Clustering properties of hierarchical self-organizing maps. J Math Imaging Vis 2(2–3): 261–272

    Article  MATH  Google Scholar 

  34. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11): 2278–2324

    Article  Google Scholar 

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

    Article  Google Scholar 

  36. Martinetz T, Schulten K (1993) Topology representing networks. Neural Netw 7(3): 507–522

    Article  Google Scholar 

  37. Mu-Chun S, Hsiao-Te C, Chien-Hsing C (2002) A novel measure for quantifying the topology preservation of self-organizing feature maps. Neural Process Lett 15: 137–145

    Article  MATH  Google Scholar 

  38. Pal NR, Bezdek JC, Tsao EC-K (1993) Generalized clustering networks and Kohonen’s self-organizing scheme. IEEE Trans Neural Netw 4(4): 549–557

    Article  Google Scholar 

  39. Pölzlbauer G, Rauber A, Dittenbach M (2005) Advanced visualization techniques for self-organizing maps with graph-based methods. In: 2nd International symposium on neural networks, pp 75–80

  40. Rynkiewicz J (2006) Self-organizing map algorithm and distortion measure. Neural Netw 19(6–7): 830–837

    Article  MATH  Google Scholar 

  41. Tasdemir K, Merényi E (2006) Data topology visualization for the self-organizing map. In: European symposium on artificial neural networks, pp 277–282

  42. Tasdemir K, Merényi E (2009) Exploiting data topology in visualization and clustering of self-organizing maps. IEEE Trans Neural Netw 20(4): 549–562

    Article  Google Scholar 

  43. Theodoridis Y (1996) Spatial datasets—an unofficial collection. http://www.dias.cti.gr/~ytheod/research/datasets/spatial.html. Last Accessed 2007

  44. UCI repository of machine learning databases (1991) http://archive.ics.uci.edu/ml/datasets/Letter+Recognition

  45. UCI repository of machine learning databases. http://archive.ics.uci.edu/ml/datasets/Insurance+Company+Benchmark+%28COIL+2000%29

  46. Vesanto J (2000) Using SOM in data mining. Licentiate’s Thesis, Department of Computer Science and Engineering, Helsinki University of Technology

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

    Article  Google Scholar 

  48. Vesanto J, Sulkava M, Hollmen J (2003) On the decomposition of the self-organizing map distortion measure. In: Proceedings of the workshop on self-organizing maps, pp 11–16

  49. Vidaurre D, Muruzábal J (2007) A quick assessment of topology preservation for SOM structures. IEEE Trans Neural Netw 18(5): 1524–1528

    Article  Google Scholar 

  50. Villmann T, Herrmann M, Der R, Martinetz M (1997) Topology preservation in self-organising feature maps: exact definition and measurement. IEEE Trans Neural Netw 8(2):256–266

    Google Scholar 

  51. Wu S, Chow TWS (2004) Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density. Pattern Recognit 37(2): 175–188

    Article  MATH  Google Scholar 

  52. Xie XL, Beni GA (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 3(8): 841–846

    Article  Google Scholar 

  53. Xu P, Chang CH, Paplinski A (2005) Self-organizing topological tree for online vector quantization and data clustering. IEEE Trans Syst Man Cybern B 35(3): 515–526

    Article  Google Scholar 

  54. Yu Y, Alahakoon D (2006) Batch implementation of growing self-organizing map. In: International conference on computational intelligence for modelling control and automation, and international conference on intelligent agents, web technologies and Internet commerce

  55. Zhang L, Merényi E (2006) Weighted differential topographic function: a refinement of topographic function. In: 14th European symposium on artificial neural networks, pp 13–18

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Authors and Affiliations

  1. REGIM: REsearch Groups on Intelligent Machines, National Engineering School of Sfax (ENIS), University of Sfax, BP 1173, 3038, Sfax, Tunisia

    Thouraya Ayadi, Tarek M. Hamdani & Adel M. Alimi

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  1. Thouraya Ayadi
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  2. Tarek M. Hamdani
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  3. Adel M. Alimi
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Correspondence to Thouraya Ayadi.

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Ayadi, T., Hamdani, T.M. & Alimi, A.M. MIGSOM: Multilevel Interior Growing Self-Organizing Maps for High Dimensional Data Clustering. Neural Process Lett 36, 235–256 (2012). https://doi.org/10.1007/s11063-012-9233-1

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