K-means clustering seeds initialization based on centrality, sparsity, and isotropy

Pilsung Kang, Sungzoon Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

K-Means is the most commonly used clustering algorithm. Despite its numerous advantages, it has a crucial drawback: the final cluster structure entirely relies on the choice of initial seeds. In this paper, a new seeds initialization algorithm based on centrality, sparsity, and isotropy is proposed. Preliminary experiments show that the proposed algorithm not only resulted in better clustering structures, but also accelerated the convergence.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - IDEAL 2009 - 10th International Conference, Proceedings
Pages109-117
Number of pages9
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009 - Burgos, Spain
Duration: 2009 Sep 232009 Sep 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5788 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009
CountrySpain
CityBurgos
Period09/9/2309/9/26

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Kang, P., & Cho, S. (2009). K-means clustering seeds initialization based on centrality, sparsity, and isotropy. In Intelligent Data Engineering and Automated Learning - IDEAL 2009 - 10th International Conference, Proceedings (pp. 109-117). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5788 LNCS). https://doi.org/10.1007/978-3-642-04394-9_14