Evaluation of selected approaches to clustering  categorical variables

Zdeněk  Šulc; Hana  Řezanková

doi:https://doi.org/10.59170/stattrans-2014-039

Evaluation of selected approaches to clustering categorical variables

Zdeněk Šulc Department of Statistics and Probability, University of Economics, Prague. W. Churchill sq.4, 130 67 Praha 3, Czech Republic. , Hana Řezanková Department of Statistics and Probability, University of Economics, Prague. W. Churchill sq.4, 130 67 Praha 3, Czech Republic. Statistics in Transition new series, vol. 15, 2014, 4, pages: 591-610 Published online: 1 December 2014 https://doi.org/10.59170/stattrans-2014-039

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ABSTRACT

This paper focuses on recently proposed similarity measures and their performance in categorical variable clustering. It compares clustering results using three recently developed similarity measures (IOF, OF and Lin measures) with results obtained using two association measures for nominal variables (Cramér’s V and the uncertainty coefficient) and with the simple matching coefficient (the overlap measure). To eliminate the influence of a particular linkage method on the structure of final clusters, three linkage methods are examined (complete, single, average). The created groups (clusters) of variables can be considered as the basis for dimensionality reduction, e.g. by choosing one of the variables from a given group as a representative for the whole group. The quality of resulting clusters is evaluated by the within-cluster variability, expressed by the WCM coefficient, and by dendrogram analysis. The examined similarity measures are compared and evaluated using two real data sets from a social survey

KEYWORDS

variable clustering, nominal variables, association measures, similarity measures

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ŠULC, Z., ŘEZANKOVÁ, H., (2014). Evaluation of recent similarity measures for categorical data. In: Proceedings of the 17th International Conference Applications of Mathematics and Statistics in Economics. Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu, Wroclaw, pp. 249–258. Available at: < http://www.amse.ue.wroc.pl/papers/Sulc,Rezankova.pdf> [Accessed 5 November 2014]