张淑清, 黄震坤, 冯铭. 一种优化的改进k_means算法[J]. 微电子学与计算机, 2015, 32(12): 36-39.
引用本文: 张淑清, 黄震坤, 冯铭. 一种优化的改进k_means算法[J]. 微电子学与计算机, 2015, 32(12): 36-39.
ZHANG Shu-qing, HUANG Zhen-kun, FENG Ming. An Optimized k_means Algorithm[J]. Microelectronics & Computer, 2015, 32(12): 36-39.
Citation: ZHANG Shu-qing, HUANG Zhen-kun, FENG Ming. An Optimized k_means Algorithm[J]. Microelectronics & Computer, 2015, 32(12): 36-39.

一种优化的改进k_means算法

An Optimized k_means Algorithm

  • 摘要: 传统的k_means算法随机地选择初始中心,导致最终聚类结果陷入局部最优且准确率低.min_max算法针对初始中心随机选择的缺点提出了改进.但原始的k_means和min_max算法都忽略了利用原空间欧式距离度量相似性的不合理性.对此提出改进算法,利用映射函数将输入向量转换到特征空间,在min_max算法基础上确定初始中心后,根据特征空间中的欧式距离来进行分类.实验证明了改进算法的有效性,在iris和wine数据集上获得了92.86%和72.34%的分类准确率.

     

    Abstract: The traditional k_means randomly selects initial cluster centers and divides the sample points by Euclidean distance in the original space, so the accuracy classification isnt't enough. The min_max algorithm obtatins more improvement than the traditional algorithm.However,the traditional algorithm and the min_max algorithm neglect the irrationality of the classification by Euclidean distance.The improved algorithm maps the input vectors into the feature space and determines the initial centers,at last clusters by the distance between two points in the feature space. The improved algorithm is evaluated on available datasets called iris and wine.

     

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