A comparative analysis on the bisecting K-means and the PDDP clustering algorithms

Authors:
Sergio M. Savaresi;Daniel L. Boley
Affiliations:
Dipartimento di Elettronica e Informazione, Politecnico di Milano, Piazza L. da Vinci, 32, 20133, Milan, Italy. Tel.: +39 02 2399 3545/ Fax: +39 02 2399 3412/ E-mail: savaresi@elet.polimi.it;Department of Computer Science and Engineering, University of Minnesota, 4-192 EE/CSci, 200 Union St SE, Minneapolis, MN 55455, USA. E-mail:boley@cs.umn.edu
Venue:
Intelligent Data Analysis
Year:
2004

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Abstract

This paper deals with the problem of clustering a data set. In particular, the bisecting divisive partitioning approach is here considered. We focus on two algorithms: the celebrated K-means algorithm, and the recently proposed Principal Direction Divisive Partitioning (PDDP) algorithm. A comparison of the two algorithms is given, under the assumption that the data set is uniformly distributed within an ellipsoid. In particular, the dynamic behavior of the K-means iterative procedure is studied and discussed; for the 2-dimensional case a closed-form model is given.