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Towards a Classification of Binary Similarity Measures

  • Ivan Ramirez Mejia
  • Ildar BatyrshinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10632)

Abstract

Similarity measures for binary variables are used in many problems of machine learning, pattern recognition and classification. Currently, the dozens of similarity measures are introduced and the problem of comparative analysis of these measures appears. One of the methods used for such analysis is clustering of similarity measures based on correlation between data similarity values obtained by different measures. The paper proposes the method of comparative analysis of similarity measures based on the set theoretic representation of these measures and comparison of algebraic properties of these representations. The results show existing relationship between results of clustering and the classification of measures by their properties. Due to the results of clustering depend on the clustering method and on data used for measuring correlation between measures we conclude that the classification based on the proposed properties of similarity measures is more suitable for comparative analysis of similarity measures.

Keywords

Similarity measure Binary data Contingency table Clustering 

Notes

Acknowledgements

The work is partially supported by the projects SIP 20171344, BEIFI of IPN and 283778 of CONACYT.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Centro de Investigación en ComputaciónInstituto Politécnico NacionalCiudad de MéxicoMexico

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