计算机工程与应用2018，Vol.54Issue(8)：131-136,6.DOI:10.3778/j.issn.1002-8331.1611-0427
基于数据场的类簇中心选取及其聚类
Clustering center selection and clustering based on data field
摘要
Abstract
In view of the existing clustering algorithms with widespread low clustering quality, parameter dependency and outlier effects obvious,in this paper,a clustering method based on the field data is proposed.The algorithm has its basis in the assumptions that cluster centers are surrounded by neighbors with lower local potential and that they are at a relatively large distance from any points with a higher local potential.According to the characteristics that the potential value of isolated point is equal to zero,remove the outlier and finally the other object points are divided into larger than its potential value and nearest neighbor type of clusters, so as to achieve clustering. Simulation results show that the pro-posed algorithm is effective and has no effect on the shape of the data set,and it can find out clusters center and outliers accurately without artificial parameters.关键词
类簇中心/数据场/聚类/孤立点Key words
cluster center/data field/clustering/outlier分类
信息技术与安全科学引用本文复制引用
朱振国,冯应柱..基于数据场的类簇中心选取及其聚类[J].计算机工程与应用,2018,54(8):131-136,6.基金项目
重庆市研究生科研创新项目(No.CYS15180). （No.CYS15180）
