计算机工程与应用Issue(20)：120-125,6.DOI:10.3778/j.issn.1002-8331.1310-0118
基于方向约束的对称距离聚类算法
Clustering algorithm based on symmetry distance with direction constraint
摘要
Abstract
K-means is a well studied and widely used clustering algorithm in data mining. There are many clustering algo-rithms evolved from K-means. For example, the symmetry-based version of the K-means algorithm using the point sym-metry distance as the similarity measure is proposed at recent years. In this paper, a new clustering algorithm based on point symmetry distance clustering algorithm is proposed. The direction constraint is put forward after studying the pro-perties of symmetry to enhance the description of symmetric distance and improve the accuracy of clustering. For the fact that symmetry is the relationship between two points, the strategy of convergence is modified to use the midpoint of the symmetry pair to calculate the cluster centers. The convergence performance of clustering is improved. By numerical simu-lation it shows that the proposed algorithm reaches a more accurate result with the same computational complexity as the existing one.关键词
K-means算法/聚类/对称距离/方向约束Key words
K-means algorithm/clustering/symmetry distance/direction constraint分类
信息技术与安全科学引用本文复制引用
陈强业,李际军..基于方向约束的对称距离聚类算法[J].计算机工程与应用,2015,(20):120-125,6.基金项目
浙江省公益基金项目（No.2013C31031）。 （）
