Abstract
To solve the problem that the K-means clustering algorithm is over dependent on the K value and the clustering center, we proposed an improved K-means clustering algorithm, VK-means algorithm in this paper. In the initial stage, the Voronoi diagram was adapted in the K-means algorithm to get a better K value and clustering center. By means of weighted average of K-means algorithm, the results of the criterion function is improved. This method could fast convergence and improve performance of the algorithm. The superiority of the improved algorithm was verified by experiments on Weka platform.
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Acknowledgments
This work is supported by National Nature Science Foundation of China (Grant No. 61462058) and Lanzhou Science and technology project (2014-1-127).
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Huo, J., Zhang, H. (2016). An Improved K-means Clustering Algorithm Based on the Voronoi Diagram Method. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_11
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DOI: https://doi.org/10.1007/978-3-319-41009-8_11
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Online ISBN: 978-3-319-41009-8
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