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 highlyoverlapping 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.

• Type: Conference
• Year: 2005
• Where: DASFAA
• Authors: Daxin Jiang, Jian Pei, Aidong Zhang
• Keywords: DASFAA 2005, Database, Microarray Data, Pattern-based Clustering, Quality Pattern-based Clusters