Abstract
Clustering is a commonly used unsupervised machine learning method, which automatically organized data into different clusters according to their similarities. In this paper, we carried out a throughout research on evolutionary computation based clustering. This paper proposed a sample index based encoding method, which significantly reduces the search space of evolutionary computation so the clustering algorithm converged quickly. Evolutionary computation has a good global search ability while the traditional clustering method k-means has a better capability at local search. In order to combine the strengths of both, this paper researched on the effect of initializing k-means by evolutionary computation algorithms. Experiments were conducted on five commonly used evolutionary computation algorithms. Experimental results show that the sample index based encoding method and evolutionary computation initialized k-means both perform well and demonstrate great potential.
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Yang, X., Tan, Y. (2014). Sample Index Based Encoding for Clustering Using Evolutionary Computation. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_55
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DOI: https://doi.org/10.1007/978-3-319-11857-4_55
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11856-7
Online ISBN: 978-3-319-11857-4
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