Abstract
In this paper we present a sampling approach to run the k-means algorithm on large data sets. We propose a new genetic algorithm to guide sample selection to yield better results than selecting the individuals at random and also maintains a reasonable computing time. We apply our proposal in a public mapping points data set from the 9th DIMACS Implementation Challenge.
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Luchi, D., Santos, W., Rodrigues, A., & Varejão, F. M. (2015). Genetic sampling k-means for clustering large data sets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9423, pp. 691–698). Springer Verlag. https://doi.org/10.1007/978-3-319-25751-8_83
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