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.
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© 2002 Springer-Verlag Berlin Heidelberg
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Mali, K., Mitra, S. (2002). Clustering of Symbolic Data and Its Validation. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_45
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DOI: https://doi.org/10.1007/3-540-45631-7_45
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