On the Measure of Compactness of Fuzzy Clustering

Abstract:

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This paper tested the measures of compactness of fuzzy partitions. Over the same labeled data, Fuzzy k-Means clustering algorithm generates the first partition, then the proposed revision function in (7) revises it several times to generate various fuzzy partitions with different pattern recognition rates computed by (6), finally the measures of compactness measure the compactness of each fuzzy partition. Experimental results on real data show that the measures of compactness in literatures fail to measure the compactness of a fuzzy clustering in some cases, for they argue that the fuzzy clustering with higher pattern recognition rate is less compact and worse than that with lower pattern recognition rate.

Info:

Periodical:

Advanced Materials Research (Volumes 204-210)

Edited by:

Helen Zhang, Gang Shen and David Jin

Pages:

1403-1406

DOI:

10.4028/www.scientific.net/AMR.204-210.1403

Citation:

C. S. Li and H. L. Dai, "On the Measure of Compactness of Fuzzy Clustering", Advanced Materials Research, Vols. 204-210, pp. 1403-1406, 2011

Online since:

February 2011

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Price:

$35.00

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