Volume : IV, Issue : XI, November - 2015

INTERNAL VERSUS EXTERNAL VALIDITY INDICES FOR FUZZY CLUSTRING

Dr. S. Revathy

Abstract :

 Cluster analysis aims at identifying groups of similar objects, and helps to discover distribution of patterns and interesting correlations in large data sets. A common approach for evaluation of clustering results is to use validity indices. Clustering validity approaches can use three criteria: External criteria (evaluate the result with respect to a pre–specified structure), internal criteria (evaluate the result with respect to information intrinsic to the data alone), Relative criteria (evaluate the result by compå it with result obtained through other clustering algorithm). Different types of indices are used to solve different types of problems and index selection depends on the kind of available information. This paper shows a comparison between external and internal fuzzy cluster validity indexes. Results obtained in this study indicate that internal indexes are more accurate in group determining in a given clustering structure. Five internal indexes were used in this study: PC, PE, MPC, FS, XB and Three external indexes (F–measure, Entropy, and Purity). The clusters that were used were obtained through Fuzzy c means clustering algorithm

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Article: Download PDF   DOI : 10.36106/ijsr  

Cite This Article:

Dr.S.Revathy / INTERNAL VERSUS EXTERNAL VALIDITY INDICES FOR FUZZY CLUSTRING / International Journal of Scientific Research, Vol : 4, Issue : 11 November 2015

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