A General Approach to Mining Quality Pattern-Based Clusters from Microarray Data

Abstract

Pattern-based clustering has broad applications in microarray data analysis, customer segmentation, e-business data analysis, etc. However, pattern-based clustering often returns a large number of highly-overlapping clusters, which makes it hard for users to identify interesting patterns from the mining results. Moreover, there lacks of a general model for pattern-based clustering. Different kinds of patterns or different measures on the pattern coherence may require different algorithms. In this paper, we address the above two problems by proposing a general quality-driven approach to mining top-k quality pattern-based clusters. We examine our quality-driven approach using real world microarray data sets. The experimental results show that our method is general, effective and efficient.

This research is partly supported by NSF grants DBI-0234895 and IIS-0308001, NIH grant 1 P20 GM067650-01A1, the Endowed Research Fellowship and the President Research Grant from Simon Fraser University. All opinions, findings, conclusions and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.