Generating Concept Trees from Dynamic Self-organizing Map
Self-organizing map (SOM) provides both clustering and visualization capabilities in mining data. Dynamic self-organizing maps such as Growing Self-organizing Map (GSOM) has been developed to overcome the problem of fixed structure in SOM to enable better representation of the discovered patterns. However, in mining large datasets or historical data the hierarchical structure of the data is also useful to view the cluster formation at different levels of abstraction. In this paper, we present a technique to generate concept trees from the GSOM. The formation of tree from different spread factor values of GSOM is also investigated and the quality of the trees analyzed. The results show that concept trees can be generated from GSOM, thus, eliminating the need for re-clustering of the data from scratch to obtain a hierarchical view of the data under study.
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 T. Kohonen, "The self-organizing map," Proceedings of the IEEE, vol. 78, no. 9, pp. 1464-1480-, 1990.
 D. Alahakoon, S. K. Halgamuge, and B. Srinivasan, "Dynamic selforganizing maps with controlled growth for knowledge discovery," IEEE Transactions on Neural Networks, vol. 11, no. 3, pp. 601-614, 2000.
 R. Amarasiri, D. Alahakoon, and K. A. Smith, "Hdgsom: a modified growing self-organizing map for high dimensional data clustering," in Fourth international conference on hybrid intelligent, 2004, pp. 216- 221.
 N. Ahmad, D. Alahakoon, and R. Chau, "Cluster identification and separation in the growing self-organizing map: application in protein sequence classification," Neural Computing and Applications, 2009.
 A. L. Hsu, S.-L. Tang, and S. K. Halgamuge, "An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data," Bioinformatics, vol. 19, no. 16, pp. 2131-2140, 2003.
 L. Wickramasinghe and L. Alahakoon, "A novel adaptive decision making agent architecture inspired by human behavior and brain study models," in Fourth International Conference on Hybrid Intelligent Systems, 2004. HIS -04., Dec. 2004, pp. 142-147.
 C.-Y. Chen, Y.-J. Oyang, and H.-F. Juan, "Incremental generation of summarized clustering hierarchy for protein family analysis," Bioinformatics, vol. 20, no. 16, pp. 2586-2596, 2004.
 J. Han and M. Kamber, Data mining : concepts and techniques, 2nd ed., ser. The Morgan Kaufmann series in data management systems. San Francisco, Calif. Oxford: Morgan Kaufmann ; Elsevier Science
[distributor], 2006, jiawei Han and Micheline Kamber. ill. ; 25 cm. Previous ed.: 2000.
 R. S. Michalski, "Knowledge acquisition through conceptual clustering: A theoretical framework and an algorithm for partitioning data into conjunctive concepts," International Journal of Policy Analysis and Information Systems, vol. 4, pp. 219-244, 1980.
 J. W. Tukey, Exploratory Data Analysis. Reading, MA: Addison- Wesley, 1977.
 D. S. Moore, The Basic Practice of Statistics, 2nd ed. New York: W. H. Freeman and Company, 2000.
 S. Theodoridis and K. Koutroumbas, Pattern recognition, 3rd ed. Amsterdam ; Boston: Elsevier/Academic Press, 2006, sergios Theodoridis and Konstantinos Koutroumbas.ill. ; 24 cm.Includes bibliographical references and index.
 W. M. Rand, "Objective criteria for the evaluation of clustering methods," Journal of the American Statistical Association, vol. 44, pp. 846- 850, 1971.
 P. Jaccard, "tude comparative de la distribution florale dans une portion des alpes et des jura," Bulletin del la Socit Vaudoise des Sciences Naturelles, vol. 37, pp. 547-579, 1901.
 D. H. Widyantoro, "Exploiting homogeneity of density in incremental hierarchical clustering," in Proceedings of ITB Eng. Science, vol. 39 B, no. 2, 2006, pp. 79-98.
 A. Asuncion and D. Newman, "Uci machine learning repository," 2007. (Online). Available: http://www.ics.uci.edu/ mlearn/MLRepository.html