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

ABSTRACT

Recently published studies have shown that partitional clustering algorithms that optimize certain criterion functions, which measure key aspects of inter- and intra-cluster similarity, are very effective in producing hard clustering solutions for document datasets and outperform traditional partitional and agglomerative algorithms. In this paper we study the extent to which these criterion functions can be modified to include soft membership functions and whether or not the resulting soft clustering algorithms can further improve the clustering solutions. Specifically, we focus on four of these hard criterion functions, derive their soft-clustering extensions, and present an experimental evaluation involving twelve different datasets. Our results show that introducing softness into the criterion functions tends to lead to better clustering results for most datasets.

REFERENCES

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• 3: Greg Hamerly , Charles Elkan, Alternatives to the k-means algorithm that find better clusterings, Proceedings of the eleventh international conference on Information and knowledge management, November 04-09, 2002, McLean, Virginia, USA [doi>10.1145/584792.584890]
• 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 , George Karypis, Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering, Machine Learning, v.55 n.3, p.311-331, June 2004 [doi>10.1023/B:MACH.0000027785.44527.d6]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Algorithms

General Terms:
Algorithms, Experimentation

Keywords:
document clustering, soft clustering