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Comparing clusterings using combination of the kappa statistic and entropy-based measure

Author

Listed:
  • Evženie Uglickich

    (The Czech Academy of Sciences, Institute of Information Theory and Automation)

  • Ivan Nagy

    (The Czech Academy of Sciences, Institute of Information Theory and Automation
    Czech Technical University)

  • Dominika Vlčková

    (Czech Technical University)

Abstract

The paper focuses on a problem of comparing clusterings with the same number of clusters obtained as a result of using different clustering algorithms. It proposes a method of the evaluation of the agreement of clusterings based on the combination of the Cohen’s kappa statistic and the normalized mutual information. The main contributions of the proposed approach are: (i) the reliable use in practice in the case of a small fixed number of clusters, (ii) the suitability to comparing clusterings with a higher number of clusters in contrast with the original statistics, (iii) the independence on size of the data set and shape of clusters. Results of the experimental validation of the proposed statistic using both simulations and real data sets as well as the comparison with the theoretical counterparts are demonstrated.

Suggested Citation

  • Evženie Uglickich & Ivan Nagy & Dominika Vlčková, 2019. "Comparing clusterings using combination of the kappa statistic and entropy-based measure," METRON, Springer;Sapienza Università di Roma, vol. 77(3), pages 253-270, December.
    • Handle: RePEc:spr:metron:v:77:y:2019:i:3:d:10.1007_s40300-019-00162-5
    DOI: 10.1007/s40300-019-00162-5
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    References listed on IDEAS

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    1. Schütz, Thomas & Schraven, Markus Hans & Fuchs, Marcus & Remmen, Peter & Müller, Dirk, 2018. "Comparison of clustering algorithms for the selection of typical demand days for energy system synthesis," Renewable Energy, Elsevier, vol. 129(PA), pages 570-582.
    2. Utkarsh J. Dang & Antonio Punzo & Paul D. McNicholas & Salvatore Ingrassia & Ryan P. Browne, 2017. "Multivariate Response and Parsimony for Gaussian Cluster-Weighted Models," Journal of Classification, Springer;The Classification Society, vol. 34(1), pages 4-34, April.
    3. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    4. Salvatore Ingrassia & Antonio Punzo & Giorgio Vittadini & Simona Minotti, 2015. "The Generalized Linear Mixed Cluster-Weighted Model," Journal of Classification, Springer;The Classification Society, vol. 32(1), pages 85-113, April.
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