基于改进流形距离的粗糙集k-means聚类算法-计算机工程与应用.PDF

84 2016 ，52（14） Computer Engineering and Applications 计算机工程与应用 基于改进流形距离的粗糙集k-means 聚类算法 欧 慧，夏卓群，武志伟 OU Hui, XIA Zhuoqun, WU Zhiwei 长沙理工大学 计算机与通信工程学院，长沙 410114 College of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China OU Hui, XIA Zhuoqun, WU Zhiwei. Rough k-means clustering algorithm based on improved manifold distance. Computer Engineering and Applications, 2016, 52 （14）：84-89. Abstract ：“Absolute manifold ”dataset has better performance than the “relative manifold ”one, the sick clustering perfor- mance while the parameter ρ varies with a wide range, which are the defects exited in clustering algorithm based on the manifold distance. To resolve these problems, a rough k-means clustering algorithm based on the improved manifold distance is proposed. In this algorithm, boundary data clustering problem and the clustering performance has been resolved and improved by choosing clustering center with attribute partitioning and the max-min distance method, optimizing k-means with the improved manifold distance and rough set and combining the termination of judgement conditions. The simulation results show that this algorithm can effectively improve both on the “absolute manifold”and “relative manifold ”dataset clustering, and the variation of parameters has a greater impact of the clustering performance. Key words ：k-means algorithm; max-min distance; improved manifold distance; rough set; criterion function 摘 要：针对现有的基于流形距离的聚类算法对“绝对流形”数据集较“相对流形”数据集聚类效果佳和参数 在较ρ 大范围内变化时，聚类性能较差等问题，提出基于改进流形距离的粗糙集k-means 聚类算法。该算法通过用属性划 分和最大最小距离选择初始聚类中心，以改进的流形距离和粗糙集优化k-means ，并结合终止判断条件以达到解决 边界数据聚类问题和提升聚类效果的目的。仿真结果表明：该算法对“绝对流形”和“相对流形”数据集聚类效果均 有较好改善，且参数变化对聚类性能影响较大。 关键词：k-means 算法；最大最小距离；改进流形距离；粗糙集；适应度函数 文献标志码：A 中图分类号：TP391 doi ：10.3778/j.issn. 1002-8331.1409-0250 1 引言 力和局部有哪些信誉好的足球投注网站能力强等特点被广泛使用，因以最小欧氏 聚类即把一个数据对象（或观测）划分为簇的过程