Individual approximate clusters: Methods, properties, applications

Abstract

A least-squares data approximation approach to finding individual clusters is advocated. A simple local optimization algorithm leads to suboptimal clusters satisfying some natural tightness criteria. Three versions of an iterative extraction approach are considered, leading to a portrayal of the cluster structure of the data. Of these, probably most promising is what is referred to as the incjunctive clustering approach. Applications are considered to the analysis of semantics, to integrating different knowledge aspects and consensus clustering. © 2013 Springer-Verlag.