Scalable visual assessment of cluster tendency for large data sets

Authors:
Richard J. Hathaway;James C. Bezdek;Jacalyn M. Huband
Affiliations:
Department of Mathematical Sciences, Georgia Southern University, Statesboro, GA 30460, USA;Computer Science Department, University of West Florida, Pensacola, FL 32514, USA;Computer Science Department, University of West Florida, Pensacola, FL 32514, USA
Venue:
Pattern Recognition
Year:
2006

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Abstract

The problem of determining whether clusters are present in a data set (i.e., assessment of cluster tendency) is an important first step in cluster analysis. The visual assessment of cluster tendency (VAT) tool has been successful in determining potential cluster structure of various data sets, but it can be computationally expensive for large data sets. In this article, we present a new scalable, sample-based version of VAT, which is feasible for large data sets. We include analysis and numerical examples that demonstrate the new scalable VAT algorithm.