A new initialization method for categorical data clustering

Expert Systems with Applications

Volumn 36, Issue 7, 2009, Pages 10223-10228

  Cao, Fuyuan ^a,b   Liang, Jiye ^a,b   Bai, Liang ^b  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b SHANXI UNIVERSITY   (China)

Author keywords

Density; Distance; Initial cluster center; Initialization method; k modes algorithm

Indexed keywords

DENSITY; DISTANCE; INITIAL CLUSTER CENTER; INITIALIZATION METHOD; K-MODES ALGORITHM;

CLUSTERING ALGORITHMS;

EID: 67349143085     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.01.060     Document Type: Article

References (26)

1. Bradley P.S., and Fayyad U.M. Refining initial points for K-means clustering. In: Sharlik J. (Ed). Proceedings of the 15th international conference on machine learning (ICML98) (1998), Morgan Kaufmann, San Francisco, CA 91-99
2. Bradley P.S., Mangasarian O.L., and Street W.N. Clustering via concave minimization. In: Mozer M.C., Jordan M.I., and Petsche T. (Eds). Advances in neural information processing system Vol. 9 (1997), MIT Press 368-374
3. Brendan J.F., and Delbert D. Clustering by passing messages between data points. Science 315 16 (2007) 972-976
4. Duda R.O., and Hart P.E. Pattern classification and scene analysis (1973), John Wiley and Sons, NY
5. Fisher D. Knowledge acquisition via incremental conceptual clustering. Machine Learning 2 (1987) 139-172
6. Fisher D. Iterative optimization and simplification of hierarchical clusterings. Journal of Artificial Intelligence Research 4 (1996) 147-179
7. Gan G., Wu J., and Yang Z. A genetic fuzzy k-modes algorithm for clustering categorical data. Expert Systems with Application 36 2 (2009) 1615-1620
8. Higgs R.E., Bemis K.G., Watson I.A., and Wikel J.H. Experimental designs for selecting molecules from large chemical databases. Journal of Chemical Information and Computer Sciences 37 (1997) 861-870
9. Huang Z.X. Clustering large datasets with mixed numeric and categorical values. Proceedings of the 1st Pacific Asia knowledge discovery and data mining conference (1997), World Scientific, Singapore 21-34
10. Huang Z.X. A fast clustering algorithm to cluster very large categorical data sets in data mining. Proceding SIGMOD Workshop Research Issues on Data Mining and Knowledge Discovery (1997) 1-8
11. Huang Z.X. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining Knowledge Discovery 2 3 (1998) 283-304
12. Huang Z.X., and Ng M.K. A Fuzzy k-modes algorithm for clustering categorical data. IEEE Transactions on Fuzzy Systems 7 4 (1999) 446-452
13. Khan S.S., and Ahmad A. Cluster center initialization algorithm for K-means clustering. Patter Recognition Letters 25 (2004) 1293-1302
14. Liang J.Y., and Li D.Y. Uncertainty and knowledge acquisition in information systems (2005), Science Press, Beijing, China
15. Meila M., and Heckerman D. An experimental comparison of several clustering and initialization methods. Proceedings of the 14th conference on uncertainty in artificial intelligence (1998), Morgan Kaufman, San Francisco, CA 386-395
16. Milligan G.W. An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrica 45 (1980) 325-342
17. Ng M.K., Li M.J., Huang Z.X., and He Z.Y. On the impact of dissimilarity measure in k-modes clustering algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence 29 3 (2007) 503-507
18. Pawlak Z. Rough sets-theoretical aspects of reasoning about data (1991), Kluwer Academic Publishers, Dordrecht, Boston, London
19. Pen J.M., Lozano J.A., and Larraaga P. An empirical comparison of four initalization methods for the K-means algorithm. Pattern Recognition Letter 20 (1999) 1027-1040
20. Snarey M., Terrett N.K., Willet P., and Wilton D.J. Comparison of algorithms for dissimilarity-based compound selection. Journal of Molecular Graphics and Modelling 15 (1997) 372-385
21. Sun Y., Zhu Q.M., and Chen Z.X. An iterative initial-points refinement algorithm for categorical data clustering. Pattern Recognition Letters 23 (2002) 875-884
22. UCI Machine Learning Repository (2006). .
23. Ward J.H. Hierarchical grouping to optimize an objective function. Journal of The American Statistical Association 58 (1963) 236-244
24. Wu S., Jiang Q.S., and Huang Z.X. A new initialization method for categorical data clsutering. Lecture Notes in Computer Science 4426 (2007) 972-980
25. Yang Y.M. An evaluation of statistical approaches to text categorization. Journal of Information Retrieval 1 1-2 (1999) 67-88
26. Zhang W.X., Wu W.Z., Liang J.Y., and Li D.Y. Rough set theory and method (2001), Science Press, Beijing, China