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Fuzzy Clustering Methods in Data Mining: A Comparative Case Analysis

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4 Author(s)
Raju, G. ; SCMS Sch. of Technol. & Manage. ; Thomas, B. ; Tobgay, S. ; Kumar, T.S.

The conventional clustering algorithms in data mining like k-means algorithm have difficulties in handling the challenges posed by the collection of natural data which is often vague and uncertain. The modeling of imprecise and qualitative knowledge, as well as handling of uncertainty at various stages is possible through the use of fuzzy sets. Fuzzy logic is capable of supporting to a reasonable extent, human type reasoning in natural form by allowing partial membership for data items in fuzzy subsets. Integration of fuzzy logic in data mining has become a powerful tool in handling natural data. In this paper we introduce the concept of fuzzy clustering and also the benefits of incorporating fuzzy logic in data mining. Finally this paper provides a comparative analysis of two fuzzy clustering algorithms namely fuzzy c-means algorithm and adaptive fuzzy clustering algorithm.

Published in:

Advanced Computer Theory and Engineering, 2008. ICACTE '08. International Conference on

Date of Conference:

20-22 Dec. 2008

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