Donald Bren School of Information and Computer Sciences

In this paper, I describe a large variety of clustering methods within a single framework. This paper unifies work across different fields, from biology (numerical taxonomy) to machine learning (concept formation). An important objective for this paper is to show that one can benefit by a knowledge of research across different disciplines. After describing the task from a set of different viewpoints or paradigms, I begin by describing the similarity measures or evaluation functions that form the basis of any clustering technique. Next, I describe a number of different algorithms that use these measures, and I close with a brief discussion of ways to evaluate different approaches to clustering.