Mining high-quality clusters in pattern-based clustering
Qian Ma, Jingfeng Guo · 2011
Pattern-based clustering, which capture the similarity of the patterns exhibited by objects in a subset of dimensions, has broad applications in DNA 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 huge mining results. Moreover, there lacks a general measurement to evaluate the quality of Clusters which pattern-based clustering obtained. In this paper, we discuss factors which cause highly-overlapping, make error analysis and pattern weighting, and propose qScore as a key evaluation parameters on quality of Clusters. A algorithm which based on qScore is presented to solve the problem of high-overlapping and get better quality clustering results.