Abstract
Methods of cluster analysis are well known techniques of multivariate analysis used for many years. Their main applications concern clustering objects characterized by quantitative variables. For this case various coefficients for clustering evaluation and determination of cluster numbers have been proposed. However, in some areas, i.e., for segmentation of Internet users, the variables are often nominal or ordinal as their origin in questionnaire responses. That is why we are dealing with the evaluation criteria for the case of categorical variables here. The criteria based on variability measures are proposed. Instead of variance as a measure for quantitative variables, three measures for nominal variables are considered: the variability measure based on a modal frequency, Gini’s coefficient of mutability, and the entropy. The proposed evaluation criteria are applied to a real-dataset.
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Rezankova, H., Loster, T., Husek, D. (2011). Evaluation of Categorical Data Clustering. In: Mugellini, E., Szczepaniak, P.S., Pettenati, M.C., Sokhn, M. (eds) Advances in Intelligent Web Mastering – 3. Advances in Intelligent and Soft Computing, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18029-3_18
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DOI: https://doi.org/10.1007/978-3-642-18029-3_18
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