Soft clustering criterion functions for partitional document clustering: A summary of results

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2004, Pages 246-247

  Zhao, Ying ^a   Karypis, George ^a  

^a University of Minnesota   (United States)

Author keywords

Document clustering; Soft clustering

Indexed keywords

CRITERION FUNCTIONS; DATASETS; DOCUMENT CLUSTERING; SOFT CLUSTERING;

ALGORITHMS; COMPUTATIONAL METHODS; DATA ACQUISITION; FUZZY SETS; OPTIMIZATION; PROBLEM SOLVING; VECTORS;

FUNCTIONS;

EID: 18744378018     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. E. Backer. Cluster Analysis by Optimal Decomposition of Induced Fuzzy Sets. Delft University Press, Delft, The Netherlands, 1978.
2. J.C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, 1981.
3. G. Hamerly and C. Elkan. Alternatives to the k-means algorithm that find better clusterings. In Proc. of Int'l. Conf. on Information and Knowledge Management (CIKM-02), pages 600-607p, 2002.
4. M. E. S. Mendes and L. Sacks. Evaluating fuzzy clustering for relevance-based information access. In Proc. of the IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2003, pages 648-653, May 2003.
5. Ying Zhao and George Karypis. Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning, 55(3):311-331, 2004.