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Using Hierarchical Histogram Representation for the EM Clustering Algorithm Enhancement

机译:使用分层直方图表示EM聚类算法增强

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This paper is devoted to EM clustering improvement using hierarchical multivariate histogram for probability density representation. We propose to store and operate with the image histogram by means of a special tree data structure. This allows to speed up computations in the case of multivariate input. We also answer the questions of the algorithm initialization and offer an initialization rule, which exploits the proposed histogram-tree structure. We have tested our algorithm modification and initialization rule using remote sensing images. Obtained results have confirmed that the modified algorithm is faster and the initialization rule provides better clustering in comparison with the traditional EM algorithm implementation.
机译:本文使用分层多变量直方图进行概率密度表示的分层多变量直方图,致力于EM聚类改进。我们建议通过特殊的树数据结构使用图像直方图存储和操作。这允许在多变量输入的情况下加速计算。我们还回答了算法初始化的问题,并提供了初始化规则,它利用所提出的直方图树结构。我们使用遥感图像测试了我们的算法修改和初始化规则。获得的结果已经证实,修改的算法更快,初始化规则与传统的EM算法实现相比,初始化规则提供更好的聚类。

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