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

Ying Zhao, George Karypis

Research output: Contribution to conferencePaper

17 Scopus citations

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.

Original languageEnglish (US)
Pages246-247
Number of pages2
StatePublished - Dec 1 2004
EventCIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management - Washington, DC, United States
Duration: Nov 8 2004Nov 13 2004

Other

OtherCIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management
CountryUnited States
CityWashington, DC
Period11/8/0411/13/04

Keywords

  • Document clustering
  • Soft clustering

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