Abstract
We propose a clustering algorithm using particle swarm optimization (PSO) for partitioning a set of objects in K clusters, by defining a familiy of agents-partitions, each agent is defined by K centroids in a p-dimensional space; a centroid has an associated cluster, which is defined by the allocation of the objects to the nearest centroid. The agents move in the space according to PSO principles, that is, they move with random intensity in the direction of a vector called velocity, which results from the random sum of the best past position of this agent, the best overall agent, and the last direction. We compare the performance of the method with other heuristics also proposed by the authors, and with two classical methods.
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Trejos-Zelaya, J., Villalobos-Arias, M. (2007). Partitioning by Particle Swarm Optimization. In: Brito, P., Cucumel, G., Bertrand, P., de Carvalho, F. (eds) Selected Contributions in Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73560-1_22
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DOI: https://doi.org/10.1007/978-3-540-73560-1_22
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