Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity

--Clustering is an effective technique in data mining to group a set of objects in terms of some attributes. Theoretical analyses and extensive experiments on several public datasets demonstrate the effectiveness and rationality of our proposed REFCMFS method. S a fundamental problem in machine learning, clustering is widely used for many fields, such as the network data (including Protein-Protein Interaction Networks [1], Road Networks [2], Geo-Social Network [3]), medical diagnosis [4], biological data analysis [5], environmental chemistry [6] and so on. K-Means clustering is one of the most popular techniques because of its simplicity and effectiveness, which randomly initializes the cluster centroids, assigns each sample to its nearest cluster and then updates cluster centroid itera-tively to cluster a dataset into some subsets. Over the past years, many modified versions of K-Means algorithms have been proposed, such as K-Means based Consensus clustering [7], Optimized Cartesian K-Means [8], Group K-Means [9] and so on. Jinglin Xu and Junwei Han were with the School of Automation, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China. Feiping Nie is with School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China. Xuelong Li is with School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China.