Abstract
The most unsupervised methods of classification suffer from several performance problems, especially the class number, the initialization start points and the solution quality. In this work, we propose a new approach to estimate the class number and to select a set of centers that represent, fiddly, a set of given data. Our key idea consists to express the clustering problem as a bivalent quadratic optimization problem with linear constraints. The proposed model is based on three criterions: the number of centers, the density data and the dispersion of the chosen centers. To validate our proposed approach, we use a genetic algorithm to solve the mathematical model. Experimental results applied on IRIS Data, show that the proposed solution selects an adequate centers and leads to a reasonable class number.
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© 2016 Springer International Publishing Switzerland
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Haddouch, K., Allaoui, A.E., Messaoudi, A., Moutaouakil, K.E., Dadi, E.W. (2016). Clustering Problem with 0–1 Quadratic Programming. In: El Oualkadi, A., Choubani, F., El Moussati, A. (eds) Proceedings of the Mediterranean Conference on Information & Communication Technologies 2015. Lecture Notes in Electrical Engineering, vol 381. Springer, Cham. https://doi.org/10.1007/978-3-319-30298-0_12
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DOI: https://doi.org/10.1007/978-3-319-30298-0_12
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