Partitional clustering algorithms for symbolic interval data based on single adaptive distances

Authors:
Francisco de A. T. De Carvalho;Yves Lechevallier
Affiliations:
Centro de Informática, Universidade Federal de Pernambuco, Av. Prof. Luiz Freire, s/n - Cidade Universitária - CEP, 50740-540 Recife (PE), Brazil;INRIA-Institut National de Recherche en Informatique et en Automatique Domaine de Voluceau, Rocquencourt B.P.105, 78153 Le Chesnay Cedex, France
Venue:
Pattern Recognition
Year:
2009

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Abstract

This paper introduces dynamic clustering methods for partitioning symbolic interval data. These methods furnish a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between clusters and their representatives. To compare symbolic interval data, these methods use single adaptive (city-block and Hausdorff) distances that change at each iteration, but are the same for all clusters. Moreover, various tools for the partition and cluster interpretation of symbolic interval data furnished by these algorithms are also presented. Experiments with real and synthetic symbolic interval data sets demonstrate the usefulness of these adaptive clustering methods and the merit of the partition and cluster interpretation tools.