A non-parametric method for data clustering with optimal variable weighting

Ji Won Chung, In Chan Choi

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

1 Citation (Scopus)

Abstract

Since cluster analysis in data mining often deals with large-scale high-dimensional data with masking variables, it is important to remove non-contributing variables for accurate cluster recovery and also for proper interpretation of clustering results. Although the weights obtained by variable weighting methods can be used for the purpose of variable selection (or, elimination), they alone hardly provide a clear guide on selecting variables for subsequent analysis. In addition, variable selection and variable weighting are highly interrelated with the choice on the number of clusters. In this paper, we propose a non-parametric data clustering method, based on the W-k-means type clustering, for an automated and joint decision on selecting variables, determining variable weights, and deciding the number of clusters. Conclusions are drawn from computational experiments with random data and real-life data.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning, IDEAL 2006 - 7th International Conference, Proceedings
PublisherSpringer Verlag
Pages807-814
Number of pages8
ISBN (Print)3540454853, 9783540454854
DOIs
Publication statusPublished - 2006
Event7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006 - Burgos, Spain
Duration: 2006 Sep 202006 Sep 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4224 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006
CountrySpain
CityBurgos
Period06/9/2006/9/23

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Chung, J. W., & Choi, I. C. (2006). A non-parametric method for data clustering with optimal variable weighting. In Intelligent Data Engineering and Automated Learning, IDEAL 2006 - 7th International Conference, Proceedings (pp. 807-814). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4224 LNCS). Springer Verlag. https://doi.org/10.1007/11875581_97