NDLI: A new Differential Evolution based Fuzzy Clustering for Automatic Cluster Evolution

Content Provider	IEEE Xplore Digital Library
Author	Saha, I. Maulik, U. Bandyopadhyay, S.
Copyright Year	2009
Abstract	In this paper, the problem of finding the number of optimal cluster partitions in fuzzy domain has been countered. The fact motivated us to develop an algorithm on differential evolution for automatic cluster detection from the unknown data set. Here, assignments of points to different clusters are done based on a Xie-Beni index where the euclidean distance takes into consideration. The cluster centers are encoded in the vectors, and the Xie-Beni index is used as a measure of the validity of the corresponding partition. The effectiveness of the proposed technique is demonstrated for two synthetic and two real life data sets. Superiority of the new method is demonstrated by comparing it with the variable length genetic algorithm based fuzzy clustering and well known Fuzzy C-Means algorithm.
Starting Page	706
Ending Page	711
File Size	3838519
Page Count	6
File Format	PDF
ISBN	9781424429271
DOI	10.1109/IADCC.2009.4809099
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-06
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape measurement Clustering methods Optimization methods Evolutionary computation Genetic Algorithm Partitioning algorithms Unsupervised classification Unsupervised learning Genetic algorithms Fuzzy sets Cluster validity index Fuzzy clustering Clustering algorithms Euclidean distance Differential evolution
Content Type	Text
Resource Type	Article

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