Clustering of symbolic data using the assignment-prototype algorithm

Authors:
Kelly P. Silva;Francisco A. T. de Carvalho;Marc Csemel
Affiliations:
Center of Informatics, Federal University of Pernambuco, Recife, PE, Brazil;Center of Informatics, Federal University of Pernambuco, Recife, PE, Brazil;Université Paris Dauphine, Paris and INRIA - Rocquencourt, Le Chesnay Cedex, France
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

This paper shows a fuzzy relational clustering method in order to perform the clustering of symbolic data. The presented method yields a fuzzy partition and prototype for each cluster by optimizing an adequacy criterion based on suitable dissimilarity measures. This work considers two volume-based measures that may be applied to data described by set-valued, list-valued or interval-valued symbolic variables. Experiments with real and synthetic symbolic data sets show the usefulness of the proposed approach. The accuracy of the results were assessed by the corrected Rand index and the overall error rate of classification.