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Information Theoretic Weighted Fuzzy clustering Ensemble

Computers, Materials & Continua 2021年第4期67卷 369-392页

作者： Yixuan Wang Liping Yuan Harish Garg Ali Bagherinia Hamïd Parvïn Kim-Hung Pho Zulkei Mansor Department of Electronic Information Engineering School of Information EngineeringWuhan University of TechnologyWuhanChina School of Information Engineering Wuhan University of TechnologyWuhanChina School of Information Engineering Wuhan Huaxia University of TechnologyWuhanChina School of Mathematics Thapar Institute of Engineering and TechnologyDeemed UniversityPatialaPunjabIndia Department of Computer Science Dehdasht BranchIslamic Azad UniversityDehdashtIran Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science Nourabad Mamasani BranchIslamic Azad UniversityMamasaniIran Fractional Calculus Optimization and Algebra Research GroupFaculty of Mathematics and StatisticsTon Duc Thang UniversityHo Chi Minh CityVietnam Fakulti Teknologi dan Sains Maklumat Universiti Kebangsaan Malaysia 43600 UKM BangiSelangorMalaysia

In order to improve performance and robustness of clustering,it is proposed to generate and aggregate a number of primary clusters via clustering ensemble *** clustering ensemble approaches attempt to improve the performance of fuzzy clustering ***,in these approaches,cluster(or clustering)reliability has not paid much attention *** cluster(or clustering)reliability makes these approaches weak in dealing with low-quality base clustering *** this paper,we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted co-association matrix Fuzzy C-Means(RBFCM),Reliability Based Graph Partitioning(RBGP)and Reliability Based Hyper clustering(RBHC)as three new fuzzy clustering consensus *** fuzzy clustering ensemble approach works based on fuzzy cluster unreliability *** unreliability is estimated according to an entropic criterion using the cluster labels in the entire *** do so,the new metric is dened to estimate the fuzzy cluster unreliability;then,the reliability value of any cluster is determined using a Reliability Driven cluster Indicator(RDCI).The time complexities of RBHC and RBGP are linearly proportional with thnumber of data *** and robustness of the proposed method are experimentally evaluated for some benchmark *** experimental results demonstrate efciency and suitability of the proposed method.

关键词： Fuzzy clustering ensemble cluster unreliability consensus function

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