Modified fuzzy clustering algorithm based on non-negative matrix factorization locally constrained

作     者：Li, Xiangli Fan, Xuezhen Lu, Xiyan 

作者机构：School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541004 China Guangxi Applied Mathematics Center Guilin University of Electronic Technology Guilin541004 China 

出 版 物：《Journal of Ambient Intelligence and Humanized Computing》 (J. Ambient Intell. Humanized Comput.)

年 卷 期：2023年第14卷第8期

页      面：11373-11383页

核心收录：

学科分类：1205[管理学-图书情报与档案管理] 0711[理学-系统科学] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 0835[工学-软件工程] 081101[工学-控制理论与控制工程] 0701[理学-数学] 071101[理学-系统理论] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the National Natural Science Foundation of China (11961010  61967004) 

主　　题：Non negative matrix factorization 

摘      要：The fuzzy C-means (FCM) algorithm is a classical clustering algorithm which is widely used. However, especially for high-dimensional data sets with complex structures, the large-scale calculation of FCM suffers from decreasing clustering effect. In order to improve the clustering performance, we propose two new modified fuzzy clustering algorithms—modified fuzzy clustering algorithm based on non-negative matrix factorization (MFCM-NMF) and modified fuzzy clustering algorithm based on non-negative matrix factorization with local constraint (MFCM-LCNMF). Since MFCM-NMF combines NMF with modified FCM, the algorithm can use the dimensionality reduction technology of NMF, which greatly improves the computational efficiency. MFCM-LCNMF introduces NMF with local linear constraints into modified FCM, and it has a new objective function and adopts a new algorithm for alternate iteration. In the iterative process, the new membership update formula is utilized for the samples selected by the triangle inequality, which not only reduces the amount of calculation, but also obtains a higher clustering quality. Finally, a number of experiments on many data sets verify that MFCM-NMF and MFCM-LCNMF are more effective compared with other state-of-the-art methods. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.