Clustering of Symbolic Data and Its Validation

Authors:
Kalyani Mali;Sushmita Mitra
Affiliations:
-;-
Venue:
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
Year:
2002

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Abstract

Categorical clustering of symbolic data and its validation has been studied. Symbolic objects include linguistic, nominal, boolean, and interval-type data. Clustering in this domain involves the use of symbolic similarity and dissimilarity between the objects. The optimal number of meaningful clusters are determined in the process. The effectiveness of the symbolic clustering is demonstrated on a real life benchmark dataset.