信息与控制2017，Vol.46Issue(5)：614-619,626,7.DOI:10.13976/j.cnki.xk.2017.0614
基于竞争思想的分级聚类算法
Hierarchical Clustering Algorithm Based on Competitive Learning
摘要
Abstract
We propose a new hierarchical clustering algorithm based on competition theory to solve the issue of non-convex and other complex clustering for massive data analysis with efficient computation. First, we separate the data into a number of sub-clusters according to a given rudimentary clustering radius. Then, on the basis of the first-level clustering, we establish a criterion for strengthening the inter-cluster association weight based on the idea of data competition depending on the data density between the sub-clusters. Finally, the sub-clus-ters with qualified association weights are grouped into resultant clusters to solve complex clustering problems, such as non-convex clustering. The clustering accuracy and anti-noise capability of the new hierarchical clus-tering algorithm are superior to those of the traditional K-means algorithm and density-based DBSCAN cluste-ring algorithms. Given the low complexity of the algorithm, the proposed algorithm can be used in clustering analysis of big data.关键词
分级聚类/复杂聚类/竞争算法/联系性权重/类合并Key words
hierarchical clustering/complex clustering/competition algorithm/link weight/class merging分类
信息技术与安全科学引用本文复制引用
张文倩,庄华亮,陈翔,何熊熊..基于竞争思想的分级聚类算法[J].信息与控制,2017,46(5):614-619,626,7.基金项目
浙江省公益技术研究社会发展项目(2013C33069) （2013C33069）
浙江省科技项目(2013C33083) （2013C33083）
三门县科技计划项目(12401) （12401）
