NDLI: An Evolutionary Particle Swarm Optimization algorithm for data clustering

Content Provider	IEEE Xplore Digital Library
Author	Alam, S. Dobbie, G. Riddle, P.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Auckland, Auckland (Alam, S.; Dobbie, G.; Riddle, P.)
Abstract	Clustering is an important data mining task and has been explored extensively by a number of researchers for different application areas such as finding similarities in images, text data and bio-informatics data. Various optimization techniques have been proposed to improve the performance of clustering algorithms. In this paper we propose a novel algorithm for clustering that we call evolutionary particle swarm optimization (EPSO)-clustering algorithm which is based on PSO. The proposed algorithm is based on the evolution of swarm generations where the particles are initially uniformly distributed in the input data space and after a specified number of iterations; a new generation of the swarm evolves. The swarm tries to dynamically adjust itself after each generation to optimal positions. The paper describes the new algorithm the initial implementation and presents tests performed on real clustering benchmark data. The proposed method is compared with k-means clustering- a benchmark clustering technique and simple particle swarm clustering algorithm. The results show that the algorithm is efficient and produces compact clusters.
Starting Page	1
Ending Page	6
File Size	298649
Page Count	6
File Format	PDF
ISBN	9781424427048
DOI	10.1109/SIS.2008.4668294
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-21
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Performance evaluation USA Councils Clustering algorithms Benchmark testing Partitioning algorithms Data mining Application software Particle swarm optimization Bioinformatics
Content Type	Text
Resource Type	Article

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