Abstract
Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to change behavior based on data, such as from sensor data or databases. They exist a number of authors have applied genetic algorithms (GA) to the problem of K-clustering, where the required number of clusters is known. Various algorithms are used to enable the GAs to cluster and to enhance their performance, but there is little or no comparison between the different algorithms. It is not clear which algorithms are best suited to the clustering problem, or how any adaptions will affect GA performance for differing data sets. In this article we shall compare a number of algorithms of GA appropriate for thek-clustering problem with some distributions of the collections Reuters 21578, including some used for more general grouping problem.
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Castillo, S.J.L., del Castillo, J.R.F., Sotos, L.G. (2010). Algorithms of Machine Learning for K-Clustering. In: Demazeau, Y., et al. Trends in Practical Applications of Agents and Multiagent Systems. Advances in Intelligent and Soft Computing, vol 71. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12433-4_53
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DOI: https://doi.org/10.1007/978-3-642-12433-4_53
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