融合信息瓶颈的模糊三维聚类  

Fuzzy Tri-Clustering Based on Information Bottleneck

作　　者：刘永利[1] 万兴[1] LIU Yong-li WAN Xing(School of Computer Science and Technology, Henan Polytechnic University, Henan Jiaozuo 454000 Chin)

机构地区：[1]河南理工大学计算机科学与技术学院,河南焦作454000

出　　处：《北京邮电大学学报》2016年第3期70-74,共5页Journal of Beijing University of Posts and Telecommunications

基　　金：国家自然科学基金项目(61202286);河南省高等学校青年骨干教师资助项目(2015GGJS-068)

摘　　要：为了有效处理三维列联表数据,采用模糊联合聚类算法的思想,提出一种基于信息瓶颈理论的模糊三维聚类算法(IBFTC).IBFTC算法为每个维度指定隶属度函数,可实现3个维度上的同时聚类,且在目标函数中引入信息瓶颈理论计算对象与簇之间的距离.采用MovieLens数据集对IBFTC算法进行多方面分析,结果表明,IBFTC算法可获得比现有模糊联合聚类算法更高的聚类准确率.In order to group three-dimensional data,the thought of fuzzy co-clustering was adopted,and an information bottleneck based fuzzy tri-clustering algorithm,named IBFTC,was presented. The IBFTC specifies membership function for each dimension,simultaneously generates fuzzy clusters on three dimensions and adds information bottleneck theory into objective function for measuring distances between objects and clusters. Experiments on the Movie Lens dataset evaluate the performances of IBFTC from several aspects. Experiment shows that IBFTC could achieve higher accuracy than conventional fuzzy coclustering algorithms.

关 键 词：模糊聚类 联合聚类 三维聚类 信息瓶颈 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]