计算机工程与应用2019，Vol.55Issue(6)：140-144,5.DOI:10.3778/j.issn.1002-8331.1711-0422
基于ε邻域的三支决策聚类分析
Three-Way Clustering Analysis Based on ε Neighborhood
摘要
Abstract
Traditional clustering methods are two-way clustering which assumes that a cluster must be represented by a set with crisp boundary. However, assigning uncertain elements into a cluster will increase decision risk. Three-way clus-tering puts the identified elements into the core region and the uncertain elements into the fringe region to reduce decision risk. This paper presents a strategy for converting a two-way cluster to three-way cluster using the ε neighborhood of the samples. The method shrinks the two-way clustering result to get the core region and expands the two-way clustering result to get the fringe region. The experiments using the proposed method on UCI data sets show that the strategy is effective in reducing the Davies-Bouldin-Index and increasing the average silhouette coefficient and accuracy of clustering results.关键词
三支聚类/邻域/k-means聚类/k-medoid聚类/fuzzy c-means聚类Key words
three-way clustering/neighborhood/k-means clustering/k-medoid clustering/fuzzy c-means clustering分类
信息技术与安全科学引用本文复制引用
刘强,施虹,王平心,杨习贝..基于ε邻域的三支决策聚类分析[J].计算机工程与应用,2019,55(6):140-144,5.基金项目
国家自然科学基金(No.11661081) （No.11661081）
云南省科技计划重点项目(No.2017FA032). （No.2017FA032）
