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Title: Clustering with niching genetic K-means algorithm
Authors: Sheng, W
Tucker, A
Liu, X
Issue Date: 2004
Publisher: Heidelberg: Springer
Citation: Genetic and Evolutionary Computation – GECCO Jun 2004
Abstract: GA-based clustering algorithms often employ either simple GA, steady state GA or their variants and fail to consistently and efficiently identify high quality solutions (best known optima) of given clustering problems, which involve large data sets with many local optima. To circumvent this problem, we propose Niching Genetic K-means Algorithm (NGKA) that is based on modified deterministic crowding and embeds the computationally attractive k-means. Our experiments show that NGKA can consistently and efficiently identify high quality solutions. Experiments use both simulated and real data with varying size and varying number of local optima. The significance of NGKA is also shown on the experimental data sets by comparing through simulations with Genetically Guided Algorithm (GGA) and Genetic K-means Algorithm (GKA).
ISBN: 978-3-540-22343-6
ISSN: 1611-3349
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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