Grid-Based and Information Entropy-Based Clustering Algorithm for Multi-Density
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    Abstract:

    Although many existing clustering algorithm can find the arbitrary shape and different size clusters, but it is difficult to obtain satisfactory results for multi-density data set. In order to improve the quality and efficiency of clustering algorithm, the paper presents a new improving precision clustering algorithm based on grid and information entropy, which through information entropy which carried by the different densities of grid to automatically calculate the density threshold, and then identify different clusters in the multi-density data set. Experiments show that the algorithm can wipe off the noise effectively and find out the multi-density clusters that have better clustering results.

    Reference
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周悦来,谭建豪.基于网格和信息熵的多密度聚类算法.计算机系统应用,2011,20(10):189-192

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History
  • Received:March 03,2011
  • Revised:March 26,2011
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