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

Sergio M. Savaresi, Daniel L. Boley

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)345-362
Number of pages18
JournalIntelligent Data Analysis
Volume8
Issue number4
StatePublished - Dec 1 2004

Keywords

  • K-means
  • principal direction divisive partitioning
  • unsupervised clustering

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