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Research on a New Clustering Validity Index Based on Data Mining

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Frontier Computing (FC 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 542))

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Abstract

Clustering analysis has made great achievements in the development process, but there are still many problems in it. In this paper, the question on determining optimal number of clusters in cluster analysis is studied mainly. The KMIBCP (K-means Intra-Between-cluster partition) in K-means clustering algorithm is proposed. KMIBCP algorithm uses IBCP (Intra-Between-cluster partition) validity index to analyze the validity of clustering results produced by K-means clustering algorithm, and to determine optimal number of clusters. The experimental results on artificial datasets verify the effectiveness of the proposed algorithm.

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Author information

Authors and Affiliations

  1. School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Chaobo Zhang

Authors
  1. Chaobo Zhang
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Corresponding author

Correspondence to Chaobo Zhang .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung, Taiwan

    Jason C. Hung

  2. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Japan

    Neil Y. Yen

  3. Department of Innovative Information and Technology, Tamkang University, Yilan County, Taiwan

    Lin Hui

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Zhang, C. (2019). Research on a New Clustering Validity Index Based on Data Mining. In: Hung, J., Yen, N., Hui, L. (eds) Frontier Computing. FC 2018. Lecture Notes in Electrical Engineering, vol 542. Springer, Singapore. https://doi.org/10.1007/978-981-13-3648-5_220

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