Abstract
The locality sensitive k-means clustering has been proposed recently. However, it performance depends greatly on the choice of the initial centers and only proper initial centers enable this clustering approach to produce a better accuracies. In this paper, an evolutionary locality sensitive k-means clustering method is presented. This new approach uses the genetic algorithms for finding its initial centers by minimizing the Davies Bouldin clustering validity index regarded as the fitness function. To investigate the effective of our approach, some experiments are done on several datasets. Experimental results show that the proposed method can get the clustering performance significantly compared to other clustering algorithms.
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Gu, L. (2013). A Locality Sensitive K-Means Clustering Method Based on Genetic Algorithms. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_14
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DOI: https://doi.org/10.1007/978-3-642-38715-9_14
Publisher Name: Springer, Berlin, Heidelberg
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