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Using Genetic Algorithm for Selection of Initial Cluster Centers for the K-Means Method

  • Wojciech Kwedlo
  • Piotr Iwanowicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6114)

Abstract

The K-means algorithm is one of the most widely used clustering methods. However, solutions obtained by it are strongly dependent on initialization of cluster centers. In the paper a novel genetic algorithm, called GAKMI (Genetic Algorithm for the K-Means Initialization), for the selection of initial cluster centers is proposed. Contrary to most of the approaches described in the literature, which encode coordinates of cluster centers directly in a chromosome, our method uses binary encoding. In this encoding bits set to one select elements of the learning set as initial cluster centers. Since in our approach not every binary chromosome encodes a feasible solution, we propose two repair algorithms to convert infeasible chromosomes into feasible ones. GAKMI was tested on three datasets, using varying number of clusters. The experimental results are encouraging.

Keywords

Genetic Algorithm Feature Vector Cluster Center Binary String Initial Center 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Wojciech Kwedlo
    • 1
  • Piotr Iwanowicz
    • 1
  1. 1.Faculty of Computer ScienceBiałystok University of TechnologyBiałystokPoland

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