On the impact of dissimilarity measure in κ-modes clustering algorithm

Kwok Po NG*, Mark Junjie Li, Joshua Zhexue Huang, Zengyou He

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

176 Citations (Scopus)


This correspondence describes extensions to the κ-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical objects, a heuristic approach was developed in [4], [12] which allows the use of the k-modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k-modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework.

Original languageEnglish
Pages (from-to)503-507
Number of pages5
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number3
Publication statusPublished - Mar 2007

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

User-Defined Keywords

  • κ-modes algorithm
  • Categorical data
  • Clustering
  • Data mining


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