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

Evženie Uglickich (), Ivan Nagy () and Dominika Vlčková ()
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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
Dominika Vlčková: Czech Technical University

METRON, 2019, vol. 77, issue 3, No 6, 253-270

Abstract: 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.

Keywords: Comparing clusterings; Clusters agreement; $$\kappa _{\max }$$ κ max statistic; Normalized mutual information (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:metron:v:77:y:2019:i:3:d:10.1007_s40300-019-00162-5

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DOI: 10.1007/s40300-019-00162-5

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