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.
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Abdule-Wahab, R.S., Monmarché, N., Slimane, M., Fahdil, M.A., Saleh, H.H. (2006). A Scatter Search Algorithm for the Automatic Clustering Problem. In: Perner, P. (eds) Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining. ICDM 2006. Lecture Notes in Computer Science(), vol 4065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11790853_28
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DOI: https://doi.org/10.1007/11790853_28
Publisher Name: Springer, Berlin, Heidelberg
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