Abstract
In this chapter we empirically compare the performance of three different approaches to partitional clustering. These are iterative hillclimbing algorithms, genetic algorithms and estimation of distribution algorithms. Emphasis is placed on their ability to avoid local maxima and also on the simplicity of setting good parameters for them.
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Roure, J., Larrañaga, P., Sangüesa, R. (2002). An Empirical Comparison Between K-Means, GAs and EDAs in Partitional Clustering. In: Larrañaga, P., Lozano, J.A. (eds) Estimation of Distribution Algorithms. Genetic Algorithms and Evolutionary Computation, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1539-5_17
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DOI: https://doi.org/10.1007/978-1-4615-1539-5_17
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