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The Computer Journal 1997 40(9):547-554; doi:10.1093/comjnl/40.9.547
© 1997 by British Computer Society
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Genetic Algorithms for Large-Scale Clustering Problems

P. Fränti1, J. Kivijärvi2, T. Kaukoranta2 and O. Nevalainen2

1 Department of Computer Science, University of Joensuu, PB 111, FIN-80101 Joensuu, Finland Email: franti{at}cs.joensuu.fi, 2 Turku Centre for Computer Science (TUCS), Department of Computer Science, University of Turku, Lemminkäisenkatu 14 A FIN-20520 Turku, Finland

We consider the clustering problem in the case where the distances between elements are metric and both the number of attributes and the number of clusters are large. In this environment the genetic algorithm approach gives high quality clusterings, but at the expense of long running time. Three new and efficient crossover techniques are introduced her. The hybridization of the genetic algorithm and k-means algorithm is discussed.


Received April 28, 1997. revised November 6, 1997.


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