Document clustering with grouping and chaining algorithms

Authors:
Yllias Chali;Soufiane Noureddine
Affiliations:
Department of Computer Science, University of Lethbridge;Department of Computer Science, University of Lethbridge
Venue:
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Year:
2005

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Abstract

Document clustering has many uses in natural language tools and applications. For instance, summarizing sets of documents that all describe the same event requires first identifying and grouping those documents talking about the same event. Document clustering involves dividing a set of documents into non-overlapping clusters. In this paper, we present two document clustering algorithms: grouping algorithm, and chaining algorithm. We compared them with k-means and the EM algorithms. The evaluation results showed that our two algorithms perform better than the k-means and EM algorithms in different experiments.