计算机应用与软件2011，Vol.28Issue(5)：139-141,3.
一种改进的图聚类的相异度度量方法
AN IMPROVED DISSIMILARITY METRICS APPROACH FOR GRAPH CLUSTERING
摘要
Abstract
In this paper the agglomerative clustering concept is employed to conduct the clustering analysis, by combining in circulation two categories with least distance, the problem of the max-min correlativity graph clustering method is improved, of which when aiming at the situation that in the graph two neighbouring nodes both have quite big correlativity but the same nodes they connecting to are extremely few,then usual clustering method is hard to solve it that the two nodes are at big correlativity whereas actually their similarities are low. At last,the correctness and effectiveness of the agglomerative clustering algorithm is validated with experiment.关键词
相异度/度量/层次聚类方法/模块性Key words
Dissimilarity/ Metrics /Method of hierarchical clustering/ Modularity引用本文复制引用
王小黎..一种改进的图聚类的相异度度量方法[J].计算机应用与软件,2011,28(5):139-141,3.基金项目
河南省自然科学基金(0411011400) （0411011400）
