Abstract
Although various dissimilarity functions for symbolic data clustering are available in the literature, little attention has thus far been paid to making a comparison between such different distance measures. This paper presents a comparative study of some well known dissimilarity functions treating symbolic data. A version of the fuzzy c-means clustering algorithm is used to create groups of individuals characterized by symbolic variables of mixed types. The proposed approach provides a fuzzy partition and a prototype for each cluster by optimizing a criterion dependent on the dissimilarity function chosen. Experiments involving benchmark data sets are carried out in order to compare the accuracy of each function. To analyse the results, we apply an external criterion that compares different partitions of a same data set.
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da Silva, A., Lechevallier, Y., de Carvalho, F. (2009). Comparing Clustering on Symbolic Data. In: Nedjah, N., de Macedo Mourelle, L., Kacprzyk, J., França, F.M.G., de De Souza, A.F. (eds) Intelligent Text Categorization and Clustering. Studies in Computational Intelligence, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85644-3_5
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DOI: https://doi.org/10.1007/978-3-540-85644-3_5
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