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Criterion Functions for Clustering on High-Dimensional Data

  • Y. Zhao
  • G. Karypis
Chapter

Summary

In recent years, we have witnessed a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intranets. This has led to an increased interest in developing methods that can help users to effectively navigate, summarize, and organize this information with the ultimate goal of helping them to find what they are looking for. Fast and high-quality document clustering algorithms play an important role toward this goal as they have been shown to provide both an intuitive navigation/browsing mechanism by organizing large amounts of information into a small number of meaningful clusters as well as to greatly improve the retrieval performance either via cluster-driven dimensionality reduction, term-weighting, or query expansion. This ever-increasing importance of document clustering and the expanded range of its applications led to the development of a number of new and novel algorithms with different complexity-quality trade-offs. Among them, a class of clustering algorithms that have relatively low computational requirements are those that treat the clustering problem as an optimization process, which seeks to maximize or minimize a particular clustering criterion function defined over the entire clustering solution.

This chapter provides empirical and theoretical comparisons of the performance of a number of widely used criterion functions in the context of partitional clustering algorithms for high-dimensional datasets. The comparisons consist of a comprehensive experimental evaluation involving 15 different datasets, as well as an analysis of the characteristics of the various criterion func-break tions and their effect on the clusters they produce. Our experimental results show that there is 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 of the criterion function shows that their 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.

Keywords

Criterion Function Cluster Solution Document Cluster Document Vector Loose Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Y. Zhao
    • 1
  • G. Karypis
    • 2
  1. 1.Department of Computer Science and EngineeringUniversity of MinnesotaMinneapolisUSA
  2. 2.Department of Computer Science and Engineering and Digital Technology Center and Army HPC Research CenterUniversity of MinnesotaMinneapolisUSA

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