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DETECTION OF LINE-SYMMETRY CLUSTERS

机译:线对称群集的检测

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摘要

Many real-world and man-made objects are symmetry. Therefore, it is reasonable to assume that some kinds of symmetry may exist in data clusters. The most common type of symmetry is line symmetry. In this paper, we propose a line symmetry distance measure. Based on the proposed line symmetry distance, a modified version of the K-means algorithm can be used to partition data into clusters with different geometrical shapes. Several data sets are used to test the performance of the proposed modified version of the K-means algorithm incorporated with the line symmetry distance.
机译:许多现实世界和人造对象都是对称的。因此,合理地假设数据群集中可能存在某些对称性。最常见的对称类型是线对称。在本文中,我们提出了线对称距离测度。基于建议的线对称距离,可以使用K-means算法的修改版本将数据划分为具有不同几何形状的簇。几个数据集用于测试结合了线对称距离的K-means算法的改进版本的性能。

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