Modified adaptive resonance theory network for mixed data based on distance hierarchy

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3994 LNCS - IV, Issue , 2006, Pages 757-764

  Hsu, Chung Chian ^a   Huang, Yan Ping ^a,b   Hsiao, Chieh Ming ^a  

^a NATIONAL YUNLIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b CHIN MIN INSTITUTE OF TECHNOLOGY   (Taiwan)

Author keywords

Adaptive resonance theory network (ART); Clustering algorithm; Data mining; Distance hier archy; Software engineering

Indexed keywords

ADAPTIVE SYSTEMS; ALGORITHMS; DATA MINING; DATA REDUCTION; NEURAL NETWORKS; NUMERICAL METHODS; SOFTWARE ENGINEERING;

ADAPTIVE RESONANCE THEORY NETWORK (ART); CLUSTERING ALGORITHMS; DISTANCE HIERARCHY; INFORMATION SYSTEMS;

HIERARCHICAL SYSTEMS;

EID: 33746632119     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11758549_102     Document Type: Conference Paper

References (31)

1. Jain, A., and Dubes, R.: Algorithms for clustering Data. Prentice-Hall, Englewood Cliffs. NJ. (1988).
2. Hsu, C. C.: Generalizing Self-Organizing Map for Categorical Data. IEEE Transactions on Neural Networks. (2006, In press).
3. Hsu, C. C. and Wang, S. H.: An Integrated Framework for Visualized and Exploratory Pattern Discovery in Mixed Data. IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 2. (2006) 161-173.
4. Anderberg, M.: Cluster Analysis for Applications. Academic Press, New York. (1973).
5. Rasmussen, E.: Clustering Algorithms. Information Retrieval: Data Structures & Algorithms. William B. Frakes and Ricardo Baeza-Yates (Eds.). Prentice Hall. (1992).
6. Salton, G. and Buckley, C.: Automatic Text Structuring and Retrieval-experiments in Automatic Encyclopedia Searching. Proceedings of the Fourteenth International ACM SIGIR Conference on Research and Development in Information Retrieval. (1991) 21-30.
7. Kadamuddi, D. and Tsai, J. P.: Clustering algorithm for parallelizing software systems in multiprocessors environment. Software Engineering, IEEE Transactions, vol. 26, Issue 4, (2000) 340-361.
8. Tian, J.: Better reliability assessment and prediction through data clustering. Software Engineering, IEEE Transactions, vol. 28, Issue 10, (2002) 997-1007.
9. Chen K., Zhang W., Zhao H., Mei H.: An approach to constructing feature models based on requirements clustering. Requirements Engineering, 2005. Proceedings. 13th IEEE International Conference. (2005) 31-40.
10. Andritsos, P. and Tzerpos, V.: Information-theoretic software clustering. Software Engineering, IEEE Transactions, vol. 31, Issue 2. (2005) 150-165.
11. Hutchens, D. H. and Basili, V.R.: System Structure Analysis: Clustering with Data Bindings. Software Engineering, IEEE Transactions, vol. 11, no. 8. (1985) 749-757.
12. Schwanke, R. W.: An Intelligent Tool for Re-Engineering Software Modularity. Proc. 13th Int'l Conf. Software Engineering. (1991) 83-92.
13. Mancoridis, S., Mitchell, B., Chen, Y. and Gansner, E.: Bunch: A Clustering Tool for the Recovery and Maintenance of Software System Structures. Proc. Int'l Conf. Software Maintenance. (1999).
14. Can, F.: Incremental clustering for dynamic information processing. ACM Transaction for Information Systems, vol. 11. (1993) 143-164.
15. Carpenter, G. A.: Distributed learning, recognition, and prediction by ART and ARTMAP neural networks. Neural Networks, vol. 10, no. 8. (1997) 1473-1494.
16. Carpenter, G. A., and Grossberg, S.: Normal and amnesic learning, recognition, and memory by a neural model of cortico-hippocampal interactions. Trends in Neuroscience, vol. 16, no. 4. (1993) 131-137.
17. Grossberg, S.: How does a brain build a cognitive code? Psychological Review, vol. 87. (1980) 1-51.
18. Grossberg, S.: The link between brain, learning, attention, and consciousness. Consciousness and Cognition, vol. 8. (1999) 1-44.
19. Grossberg, S.: How does the cerebral cortex work? Development, learning, attention, and 3D vision by laminar circuits of visual cortex. Behavioral and Cognitive Neuroscience Reviews, vol. 2, no. 1. (2003) 47-76.
20. Guha, S., Rastogi, R. and Shim, K.: ROCK: A robust clustering algorithm for categorical attributes. Proceedings of the IEEE Conference on Data Engineering. (1999) 512-521.
21. Wilson, D.R. and Martinez, T. R.: Improved heterogeneous distance functions. Journal of Artificial Intelligence Research, vol. 6. (1997) 1-34.
22. Ester, M., Kriegel, H. P., Sander, J., Wimmer, M. and Xu, X.: Incremental clustering for mining in a data warehousing environment. Proceedings of the 24th Intl. Conf. on Very Large Data Bases (VLDB). (1998) 323-333.
23. Carpenter, G. A. and Grossberg, S.: ART 2: Self-organization of stable category recognition codes for analog input patterns. Applied Optics: Special Issue on Neural Networks, vol. 26. (1987) 4919-4930.
24. Carpenter, G., Grossberg, A., S. and Rosen, D. B.: Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks, vol. 4. (1991) 759-771.
25. Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, vol. 2, no. 3. (1998) 283-304.
26. Dash, M. and Choi, K., Scheuermann, P. and Liu, H.: Feature selection for clustering - a filter solution, IEEE International Conference on Data Mining. (2002) 115-122.
27. Maulik, U. and Bandyopadhyay, S.: Performance evaluation of some clustering algorithms and validity indices. IEEE Transactions on Pattern Analysis and Machine Intelligence. (2002) 1650-1654.
28. Gluck, M. A. and Corter, J. E.: Information, uncertainty, and the utility of categories. Proceedings of the Seventh Annual Conference of the Cognitive Science Society. (1985).
29. Barbara, D., Couto, J. and Li, Y.: COOLCAT: an entropy-based algorithm for categorical clustering. Proceedings of the eleventh international conference on Information and knowledge management. (2002) 582-589.
30. Halkidi, M., Batistakis, Y. and Vazirgiannis, M.: On clustering validation techniques. Journal of Intelligent Information Systems, vol. 17. (2001) 107-145.
31. Hsu, C. C. and Chen, Y. C.: Mining of Mixed Data with Application to Catalog Marketing. Expert Systems with Applications. (2006, In press).