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2008

Automatic Clustering Using an Improved Differential Evolution Algorithm

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Automatic Clustering Using an Improved Differential Evolution Algorithm
Differential evolution (DE) has emerged as one of the fast, robust, and efficient global search heuristics of current interest. This paper describes an application of DE to the automatic clustering of large unlabeled data sets. In contrast to most of the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified. Rather, it determines the optimal number of partitions of the data "on the run." Superiority of the new method is demonstrated by comparing it with two recently developed partitional clustering techniques and one popular hierarchical clustering algorithm. The partitional clustering algorithms are based on two powerful well-known optimization algorithms, namely the genetic algorithm and the particle swarm optimization. An interesting real-world application of the proposed method to automatic segmentation of images is also reported.
Swagatam Das, Ajith Abraham, Amit Konar
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TSMC
Authors Swagatam Das, Ajith Abraham, Amit Konar
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