Data clustering with cluster size constraints using a modified k-means algorithm

Nuwan Ganganath, Chi Tsun Cheng, Chi Kong Tse

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

31 Citations (Scopus)

Abstract

Data clustering is a frequently used technique in finance, computer science, and engineering. In most of the applications, cluster sizes are either constrained to particular values or available as prior knowledge. Unfortunately, traditional clustering methods cannot impose constrains on cluster sizes. In this paper, we propose some vital modifications to the standard k-means algorithm such that it can incorporate size constraints for each cluster separately. The modified k-means algorithm can be used to obtain clusters in preferred sizes. A potential application would be obtaining clusters with equal cluster size. Moreover, the modified algorithm makes use of prior knowledge of the given data set for selectively initializing the cluster centroids which helps escaping from local minima. Simulation results on multidimensional data demonstrate that the k-means algorithm with the proposed modifications can fulfill cluster size constraints and lead to more accurate and robust results.
Original languageEnglish
Title of host publicationProceedings - 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2014
PublisherIEEE
Pages158-161
Number of pages4
ISBN (Electronic)9781479962358
DOIs
Publication statusPublished - 1 Jan 2014
Event6th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2014 - Shanghai, China
Duration: 10 Oct 201412 Oct 2014

Conference

Conference6th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2014
CountryChina
CityShanghai
Period10/10/1412/10/14

Keywords

  • constrained clustering
  • data clustering
  • data mining
  • k-means
  • size constraints

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

Cite this