Data Clustering Analysis - From Simple Groupings to Scalable Clustering With Constraints
Chi‐Hoon Lee, Andrew Foss, Osmar R. Zai͏̈ane, Weinan Wang · ERA: Education and Research Archive (University of Alberta) · 2002
Clustering is the problem of grouping data based on similarity and consists of maximizing the intra-group similarity while minimizing the iter-group similarity. While this problem has attracted the attention of many researchers for many years, we are witnessing a resurgence of interest in new clustering techniques in the data mining community. In this paper we discuss some very recent clustering approaches and recount our experience with some of these algorithms. We also present the problem of clustering in the presence of constraints and discuss the issue of cluster validation.