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论文类型：期刊论文

发表时间：2020-02-28

发表刊物：NEUROCOMPUTING

收录刊物：EI、SCIE

卷号：379

页面范围：236-249

ISSN号：0925-2312

关键字：Extreme learning machine; Neighborhood rough set; Cluster analysis; Granular computing; Fuzzy set

摘要：Clustering is an important method for data analysis. Up to now, how to develop an efficient clustering algorithm is still a critical issue. Unsupervised extreme learning machine is an effective neural network learning method which has a fast training speed. In this paper, a fuzzy granularity neighborhood extreme clustering algorithm which is based on extreme learning machine is proposed. We use fuzzy neighborhood rough set to develop a new feature selection method to eliminate redundant attributes and introduce the adaptive adjustment mechanism to solve the parameters of unsupervised extreme learning machine. Different from the existing clustering algorithms, the proposed algorithm can obtain a clustering result with minimum intra-cluster distance and maximum inter-cluster distance. The proposed algorithm and comparison algorithms are executed on the synthetic data sets and real data sets. The experimental results show that the proposed algorithm outperforms the comparison algorithms on the most data sets and the proposed algorithm is effective for clustering task. (C) 2019 Elsevier B.V. All rights reserved.