Clustering and its validation in a symbolic framework

Pattern Recognition Letters

Volumn 24, Issue 14, 2003, Pages 2367-2376

  Mali, Kalyani ^a   Mitra, Sushmita ^b  

^a UNIVERSITY OF KALYANI   (India)

^b INDIAN STATISTICAL INSTITUTE   (India)

Author keywords

Hierarchical clustering; Symbolic clustering; Symbolic data; Validity index

Indexed keywords

BENCHMARKING; BOOLEAN ALGEBRA; DATA REDUCTION; LINGUISTICS; STATISTICAL METHODS;

DATA SETS;

PATTERN RECOGNITION;

CLUSTER ANALYSIS; PATTERN RECOGNITION;

EID: 0042341570     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(03)00066-7     Document Type: Article

References (16)

1. Alahakoon D. Halgamuge S.K. Srinivasan B. Dynamic self-organizing maps with controlled growth for knowledge discovery IEEE Trans. Neural Networks 11 3 2000 601-614
2. Ben-Hur A. Elisseeff A. Guyon I. A stability based method for discovering structure in clustered data Altman R.B. Dunker A.K. Hunter L. Lauderdale K. Klein T.E. Pacific Symposium on Biocomputing 2002 6-17 World Scientific
3. Bezdek J.C. Pal N.R. Some new indexes for cluster validity IEEE Trans. Systems Man Cybernet.--Part-B 28 1998 301-315
4. Biswas G. Weinberg J.B. Fisher D.H. ITERATE: A conceptual clustering algorithm for data mining IEEE Trans. Systems Man Cybernet. - Part C: Appl. Rev. 28 1998 219-230
5. Blake, C.L., Merz, C.J., 1998. UCI repository of machine learning databases, 1998. University of California, Irvine, Dept. of Information and Computer Sciences. Available from
6. Chidananda Gowda K. Diday E. Symbolic clustering using a new dissimilarity measure Pattern Recognition 24 6 1991 567-578
7. Chidananda Gowda K. Ravi T.V. Divisive clustering of symbolic objects using the concepts of both similarity and dissimilarity Pattern Recognition 28 8 1995 1277-1282
8. Davies G.R. Yoder D. Business Statistics 1937 John Wiley - Sons, Inc. London
9. Fridlyand, J., Dudoit, S., 2001. Applications of resampling methods to estimate the number of clusters and to improve the accuracy of a clustering method. Technical Report 600, UC Berkeley, September 2001
10. Jain A.K. Dubes R.C. Algorithms for Clustering Data 1988 Prentice Hall NJ
11. Lange, T., Braun, M.L., Roth, V., Buhmann, J.M., 2002. Stability-based model selection. In: Advances in Neural Information Processing Systems
12. Mali K. Mitra S. Clustering of symbolic data and its validation Pal N.R. Sugeno M. Advances in Soft Computing - AFSS 2002 Lecture Notes in Artificial Intelligence 2002 339-344 Springer Verlag Heidelberg
13. Michalski R. Stepp R.E. Automated construction of classifications: Conceptual clustering versus numerical taxonomy IEEE Trans. Pattern Anal Machine Intell. 5 1983 396-410
14. Milligan G.W. Cooper M.C. An examination of procedures for determining the number of clusters in a data set Psychometrika 50 1985 159-179
15. Tibshirani, R., Walther, G., Botstein, D., Brown, P., 2001. Cluster validation by prediction strength. Technical report, Stanford
16. Wilson R. Martinez T.R. Improved heterogeneous distance functions J. Artificial Intelligence Res. 6 1997 1-34