Automated variable weighting in k-means type clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 27, Issue 5, 2005, Pages 657-668

  Huang, Joshua Zhexue ^a   Ng, Michael K ^a   Rong, Hongqiang ^a   Li, Zichen ^b  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b HENAN POLYTECHNIC UNIVERSITY   (China)

Author keywords

Clustering; Data mining; Feature evaluation and selection; Mining methods and algorithms

Indexed keywords

ALGORITHMS; CONVERGENCE OF NUMERICAL METHODS; ITERATIVE METHODS; MATRIX ALGEBRA; OPTIMIZATION; THEOREM PROVING; VECTORS;

CLUSTERING; CONVERGENCY THEOREM; SYNTHETIC DATA SET;

DATA MINING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; COMPARATIVE STUDY; COMPUTER SIMULATION; EVALUATION; INFORMATION RETRIEVAL; MATHEMATICAL COMPUTING; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING; STATISTICAL MODEL; VALIDATION STUDY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTER SIMULATION; INFORMATION STORAGE AND RETRIEVAL; MODELS, STATISTICAL; NUMERICAL ANALYSIS, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 18144419389     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2005.95     Document Type: Article

References (21)

1. Z. Huang, "Extensions to the k-Means Algorithms for Clustering Large Data Sets with Categorical Values," Data Ming and Knowledge Discovery, vol. 2, no. 3, pp. 283-304, 1998.
2. J. MacQueen, "Some Methods for Classification and Analysis of Multivariate Observation," Proc. Fifth Berkeley Symp. Math. Statistica and Probability, pp. 281-297, 1967.
3. P.E. Green, F.J. Carmone, and J. Kim, "A Preliminary Study of Optimal Variable Weighting in k-Means Clustering," J. Classification, vol. 7, pp. 271-285, 1990.
4. W.S. Desarbo, J.D. Carroll, L.A. Clark, and P.E. Green, "Synthesized Clustering: A Method for Amalgamating Clustering Bases with Differential Weighting Variables," Psychometrika, vol. 49, pp. 57-78, 1984.
5. G. De Soete, "Optimal Variable Weighting for Ultrametric and Additive Tree Clustering," Quality and Quantity, vol. 20, pp. 169-180, 1986.
6. G. De Soete, "OVWTRE: A Program for Optimal Variable Weighting for Ultrametric and Additive Tree Fitting," J. Classification, vol. 5, pp. 101-104, 1988.
7. E. Fowlkes, R. Gnanadesikan, and J. Kettenring, "Variable Selection in Clustering," J. Classification, vol. 5, pp. 205-228, 1988.
8. G. Milligan, "A Validation Study of a Variable Weighting Algorithm for Cluster Analysis," J. Classification, vol. 6, pp. 53-71, 1989.
9. R. Gnanadesikan, J. Kettenring, and S. Tsao, "Weighting and Selection of Variables for Cluster Analysis," J. Classification, vol. 12, pp. 113-136, 1995.
10. V. Makarenkov and B. Leclerc, "An Algorithm for the Fitting of a Tree Metric According to a Weighted Least-Squares Criterion," J. Classification, vol. 16, pp. 3-26, 1999.
11. V. Makarenkov and P. Legendre, "Optimal Variable Weighting for Ultrametric and Additive Trees and K-Means Partitioning: Methods and Software," J. Classification, vol. 18, pp. 245-271, 2001.
12. J.H. Friedman and J.J. Meulman, "Clustering Objects on Subsets of Attributes," J. Royal Statistical Soc. B., 2002.
13. D.S. Modha and W.S. Spangler, "Feature Weighting in k-Means Clustering," Machine Learning, vol. 52, pp. 217-237, 2003.
14. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, "Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications," Proc. ACM SIGMOD, pp. 94-105, June 1998.
15. J. Bezdek, "A Convergence Theorem for the Fuzzy Isodata Clustering Algorithms," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 2, no. 1, pp. 1-8, 1980.
16. Z. Huang and M. Ng, "A Fuzzy k-Modes Algorithm for Clustering Categorical Data," IEEE Trans. Fuzzy Systems, vol. 7, no. 4, pp. 446-452, 1999.
17. M. Anderberg, Cluster Analysis for Applications. Academic Press, 1973.
18. S. Selim and M. Ismail, "K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, no. 1, pp. 81-87, 1984.
19. G. Milligan and P. Isaac, "The Valiadation of Four Ultrametric Clustering Algorithms," Pattern Recognition, vol. 12, pp. 41-50,1980.
20. A. Jain and R. Dubes, Algorithms for Clustering Data. Prentice Hall, 1988.
21. G.S. Fishman, Monte Carlo: Concepts, Algorithms, and Applications, p. 19. Springer-Verlag, 1996.