A scatter search algorithm for the automatic clustering problem

Authors:
Rasha S. Abdule-Wahab;Nicolas Monmarché;Mohamed Slimane;Moaid A. Fahdil;Hilal H. Saleh
Affiliations:
Dept. of Computer Science, University of Technology, Iraq;Laboratoire d'Informatique, Université François Rabelais de Tours, Tours, France;Laboratoire d'Informatique, Université François Rabelais de Tours, Tours, France;Dept. of Computer Science, University of Technology, Iraq;Dept. of Computer Science, University of Technology, Iraq
Venue:
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Year:
2006

Citing 6
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Abstract

We present a new hybrid algorithm for data clustering. This new proposal uses one of the well known evolutionary algorithms called Scatter Search. Scatter Search operates on a small set of solutions and makes only a limited use of randomization for diversification when searching for globally optimal solutions. The proposed method discovers automatically cluster number and cluster centres without prior knowledge of a possible number of class, and without any initial partition. We have applied this algorithm on standard and real world databases and we have obtained good results compared to the K-means algorithm and an artificial ant based algorithm, the Antclass algorithm.