A Clustering Method for Categorical Ordinal Data |
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Authors: | Marco Giordan Giancarlo Diana |
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Affiliation: | 1. Department of Statistical Sciences , University of Padova , Padova, Italy giordan@stat.unipd.it;3. Department of Statistical Sciences , University of Padova , Padova, Italy |
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Abstract: | Often, categorical ordinal data are clustered using a well-defined similarity measure for this kind of data and then using a clustering algorithm not specifically developed for them. The aim of this article is to introduce a new clustering method suitably planned for ordinal data. Objects are grouped using a multinomial model, a cluster tree and a pruning strategy. Two types of pruning are analyzed through simulations. The proposed method allows to overcome two typical problems of cluster analysis: the choice of the number of groups and the scale invariance. |
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Keywords: | Bonferroni–Holm procedure Cluster tree Multinomial distribution Neighbourhood Threshold |
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