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Improved SOM Labeling Methodology for Data Mining Applications

  • Arnulfo Azcarraga
  • Ming-Huei Hsieh
  • Shan-Ling Pan
  • Rudy Setiono

Self-Organizing Maps (SOMs) have been useful in gaining insights about the information content of large volumes of data in various data mining applications. As a special form of neural networks, they have been attractive as a data mining tool because they are able to extract information from data even with very little user-intervention. However, although learning in self-organizing maps is considered unsupervised because training patterns do not need desired output information to be supplied by the user, a trained SOM often has to be labeled prior to use in many real-world applications. Unfortunately, this labeling phase is usually supervised as patterns need accompanying output information that have to be supplied by the user. Because labeled patterns are not always available or may not even be possible to construct, the supervised nature of the labeling phase restricts the deployment of SOM to a wider range of potential data mining applications. This work proposes a methodical and semi-automatic SOM labeling procedure that does not require a set of labeled patterns. Instead, nodes in the trained map are clustered and subsets of training patterns associated to each of the clustered nodes are identified. Salient dimensions per node cluster, that constitute the basis for labeling each node in the map, are then identified. The effectiveness of the method is demonstrated on a data mining application involving customer-profiling based on an international market segmentation study.

Key words: self-organizing maps, neural networks, classification, clustering

Keywords

Training Pattern Label Pattern Salient Dimension Component Plane Visual Exploration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Azcarraga AP, Hsieh M, Setiono R, 2003, Visualizing globalization: A SOM approach to customer profiling. In: Proceedings of 24th International Conference on Information Systems (ICIS), Seattle, WA. Google Scholar
  2. Azcarraga A, Yap TN, Tan J, Chua TS, 2002, Evaluating keyword selection methods for WEBSOM text archives, IEEE Transactions on Knowledge and Data Engineering, 16(3): 380-383.Google Scholar
  3. Carlson E, 1998, Real estate investment appraisal of land properties using SOM. In: Deboeck G, Kohonen T (eds), Visual explorations infinance with selforganizing maps, Springer-Verlag, London.Google Scholar
  4. Carpenter GA, Grossberg S, 1991, Pattern-recognition by self-organizing neural networks. MIT Press, Cambridge, MA.Google Scholar
  5. Clark D, Ravishankar K, 1990, A convergence theorem for Grossberg learning, Neural Networks 3(1): 87-92.CrossRefGoogle Scholar
  6. Deboeck G, Kohonen T, 1998, Visual explorations in finance with self-organizing maps, Springer-Verlag, London.MATHGoogle Scholar
  7. Deboeck G, 1998, Picking mutual funds with self-organizing maps. In: Deboeck G, Kohonen T (eds), Visual explorations in finance with self-organizing maps, Springer-Verlag, London.Google Scholar
  8. Deboeck G, 1998, Investment maps of emerging markets. In: Deboeck G, Kohonen T (eds), Visual explorations in finance with self-organizing maps, SpringerVerlag, London.Google Scholar
  9. Everitt B, 1974, Cluster analysis, Heinemann Educational Books, London.Google Scholar
  10. Fukushima K, 1980, Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift inposition, Biological Cybernetics 36: 121-136.CrossRefGoogle Scholar
  11. Hartigan JA, 1975, Clustering algorithms, Wiley-Interscience, New York.MATHGoogle Scholar
  12. Haykin S, 1998, Neural networks: a comprehensive foundation. Prentice-Hall International, 2nd Edition, Upper Saddle River, NewJersey.Google Scholar
  13. Holbrook MB, Schindler RM, 1994, Age, sex, and attitude toward the pastas predictors of consumers’ aesthetic taste for cultural products. Journal of Consumer Research 31: 412-22.Google Scholar
  14. Hsieh MH, 2002, Identifying brand image dimensionality and measuring degree of brand globalization: a cross-national study. Journal of International Marketing 10 (2): 46-67.CrossRefGoogle Scholar
  15. Kiang MY, Kumar A, 2001, An evaluation of self-organizing map networks as a robust alternative to factor analysis in data mining applications, Information Systems Research 12: 177-194.CrossRefGoogle Scholar
  16. Kiviluto K, Bergius P, 1998, Maps for analyzing failures of small andmediumsized enterprises. In: Deboeck G, Kohonen T (eds), Visual explorations in finance with self-organizing maps, Springer-Verlag, London.Google Scholar
  17. Kohonen T, 2000, Self-organization of a massive document collection, IEEE Transactions on Neural Networks 11(3): 574-585.CrossRefGoogle Scholar
  18. Kohonen T, 1982, Self-organized formation of topologically-correct feature maps, Biological Cybernetics 43: 59-69.MATHCrossRefMathSciNetGoogle Scholar
  19. Kohonen T, 1990, The self-organizing map, Proceedings of the IEEE 78:1464-1480.CrossRefGoogle Scholar
  20. Kohonen T, 1995, Self-organizing maps, Springer-Verlag, Berlin.Google Scholar
  21. Kohonen T, 1999, Kohonen maps, Elsevier, New York.Google Scholar
  22. Kuo RJ, Ho LM, Hu CM, 2002, Integration of self-organizing feature mapand kmeans algorithm for market segmentation, Computers and Operations Research 29:1475-1493.MATHCrossRefGoogle Scholar
  23. Mayer R, Lidy T, Rauber A, (2006), The map of Mozart, Proc 7th International Conference on Music Information Retrieval, Victoria,Canada, Oct 8-12.Google Scholar
  24. Merkl D, 1998, Text classification with self-organizing maps: some lessons learned, Neurocomputing 21: 61-77.CrossRefGoogle Scholar
  25. Park CW, Jaworski BJ, MacInnis DJ, 1986, Strategic brand concept- imagemanagement. Journal of Marketing 50: 135-145.CrossRefGoogle Scholar
  26. Park CW, Milberg S, Lawson R, 1991, Evaluation of brand extension:the role of product level similarity and brand concept consistency. Journal of Consumer Research 18: 185-193.CrossRefGoogle Scholar
  27. Punj G, Steward DW, 1983, Cluster analysis in marketing research: review and suggestions for applications. Journal of Marketing Research 20: 134-148.CrossRefGoogle Scholar
  28. Quinlan R, 1993, C4.5: Programs for machine learning, Morgan Kaufman, San Mateo, CA.Google Scholar
  29. Resta M, 1998, A hybrid neural network system for trading financial markets. In: Deboeck G, Kohonen T (eds), Visual explorations infinance with self-organizing maps, Springer-Verlag, London.Google Scholar
  30. Ritter H, Martinetz T, Schulten K, 1992, Neural computation and self-organizing maps (translated from German), Addison-Wesley, Reading MA.Google Scholar
  31. Rumelhart DE, Zipser D, 1986, Feature discovery by competitive learning. In: Rumelhart DE and McClelland JL (eds) Parallel and Distributed Processing, Vol 1, 151-193. MIT Press, Cambridge, CA.Google Scholar
  32. Rumelhart DE, Hinton GE, Williams RJ, 1986, Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel and Distributed Processing, Vol 1. 318-362. MITPress, Cambridge, MA.Google Scholar
  33. Schmitt B, Deboeck G, 1998, Differential patterns in consumer purchase preferences using self-organizing maps: a case study of China. In: Deboeck G, Kohonen T (eds), Visual explorations in finance withself-organizing maps, SpringerVerlag, London.Google Scholar
  34. Serrano-Cinca C, 1998, Let financial data speak for themselves. In: Deboeck G, Kohonen T (eds), Visual explorations in finance with self-organizing maps, Springer-Verlag, London.Google Scholar
  35. Shumsky S, Yarovoy AV, 1998, Self-organizing atlas of Russian banks. In: Deboeck G, Kohonen T (eds), Visual explorations in finance with self-organizing maps, Springer-Verlag, London.Google Scholar
  36. Spath H, 1980, Cluster analysis algorithms, Ellis Horwood, Chichester,England. Tulkki A, (1998), Real estate investment appraisal of buildings using SOM. In: Deboeck G, Kohonen T (eds), Visual explorations in financewith self-organizing maps, Springer-Verlag, London.Google Scholar
  37. Wedel M, Kamakura W, 1998, Market segmentation: conceptual and methodological foundations, Kluwer Academic Publishers, Boston,MA.Google Scholar
  38. Wu S, Chow T, 2005, PRSOM: A new visualization method by hybridizing multi dimensional scaling and self-organizing Map, IEEE Trans on Neural Networks 16 (6): 1362-1380.CrossRefGoogle Scholar
  39. Xu R, Wunsch D, 2005, Survey of cluster algorithms, IEEE Trans on Neural Networks, 16(3): 645-678.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Arnulfo Azcarraga
    • 1
  • Ming-Huei Hsieh
    • 2
  • Shan-Ling Pan
    • 3
  • Rudy Setiono
    • 3
  1. 1.College of Computer StudiesDe La Salle UniversityPhilippines
  2. 2.Department of International BusinessNational Taiwan UniversityTaiwan
  3. 3.School of ComputingNational University of SingaporeSingapore

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