Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering

Authors:
Ying Zhao;George Karypis
Affiliations:
Department of Computer Science Minneapolis, University of Minnesota, USA 55455;Department of Computer Science Minneapolis, University of Minnesota, USA 55455
Venue:
Machine Learning
Year:
2004

Citing 23
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Abstract

This paper evaluates the performance of different criterion functions in the context of partitional clustering algorithms for document datasets. Our study involves a total of seven different criterion functions, three of which are introduced in this paper and four that have been proposed in the past. We present a comprehensive experimental evaluation involving 15 different datasets, as well as an analysis of the characteristics of the various criterion functions and their effect on the clusters they produce. Our experimental results show that there are a set of criterion functions that consistently outperform the rest, and that some of the newly proposed criterion functions lead to the best overall results. Our theoretical analysis shows that the relative performance of the criterion functions depends on (i) the degree to which they can correctly operate when the clusters are of different tightness, and (ii) the degree to which they can lead to reasonably balanced clusters.