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A New Clustering Validity Index for Fuzzy C Means Algorithm Based on Measure Of Disparity

Authors: ALAO Olujimi

Publication Type: Journal article

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Abstract

Cluster validity indexes have been used to evaluate the fitness of partitions produced by clustering algorithms. This paper presents a new validity index for fuzzy clustering called inter-cluster and intra-cluster separation (IC2S) index. Therefore, we proposed the function of disparity which combines the intra and inter-cluster separation existing between the clusters. The results of comparative study show that the proposed IC2S index has high ability in producing a good cluster number estimate. This performance is achieved by taking into consideration the existing disparity between clusters. To assess the new validation index, two data sets (Fisherâ€™s IRIS and Butterfly data set) were used and the results show that IC2S outperforms other clustering validation index for fuzzy c-means.

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ALAO,O. . (0000). A New Clustering Validity Index for Fuzzy C Means Algorithm Based on Measure Of Disparity, 0 (), 0-0.

ALAO,O. . "A New Clustering Validity Index for Fuzzy C Means Algorithm Based on Measure Of Disparity" 0, no (), (0000): 0-0.

ALAO,O. and . (0000). A New Clustering Validity Index for Fuzzy C Means Algorithm Based on Measure Of Disparity, 0 (), pp0-0.

ALAOO, . A New Clustering Validity Index for Fuzzy C Means Algorithm Based on Measure Of Disparity. 0000, 0 ():0-0.

ALAO,Olujimi , . "A New Clustering Validity Index for Fuzzy C Means Algorithm Based on Measure Of Disparity", 0 . (0000) : 0-0.

A.Olujimi , "A New Clustering Validity Index for Fuzzy C Means Algorithm Based on Measure Of Disparity" vol.0, no., pp. 0-0, 0000.

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