A multi-view K-multiple-means clustering method  

多视图K-多均值聚类算法

作　　者：ZHANG Nini GE Hongwei 张倪妮;葛洪伟(江南大学人工智能与计算机学院,江苏无锡214122;江南大学江苏省模式识别与计算智能实验室,江苏无锡214122)

机构地区：[1]School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China [2]Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi 214122, China

出　　处：《Journal of Measurement Science and Instrumentation》2021年第4期405-411,共7页测试科学与仪器（英文版）

基　　金：National Youth Natural Science Foundationof China(No.61806006);Innovation Program for Graduate of Jiangsu Province(No.KYLX160-781);Project Supported by Jiangsu University Superior Discipline Construction Project。

摘　　要：The K-multiple-means(KMM)retains the simple and efficient advantages of the K-means algorithm by setting multiple subclasses,and improves its effect on non-convex data sets.And aiming at the problem that it cannot be applied to the Internet on a multi-view data set,a multi-view K-multiple-means(MKMM)clustering method is proposed in this paper.The new algorithm introduces view weight parameter,reserves the design of setting multiple subclasses,makes the number of clusters as constraint and obtains clusters by solving optimization problem.The new algorithm is compared with some popular multi-view clustering algorithms.The effectiveness of the new algorithm is proved through the analysis of the experimental results.

关 键 词：K-multiple-means(KMM)clustering weight parameters multi-view K-multiple-means(MKMM)method 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]