Clustering with Niching Genetic K-means Algorithm

Sheng, Weiguo and Tucker, A. and Liu, X. (2004) Clustering with Niching Genetic K-means Algorithm. In: Lecture Notes in Computer Science 3103. SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY , pp. 162-173. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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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).

Item Type: Book section
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics (see also: telecommunications), > TK7880 Applications of electronics (inc industrial & domestic) > TK7885 Computer engineering
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Yiqing Liang
Date Deposited: 01 Oct 2008 14:39
Last Modified: 25 Jun 2014 08:32
Resource URI: (The current URI for this page, for reference purposes)
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