Indexed by:Journal Papers

Date of Publication:2020-02-28

Journal:NEUROCOMPUTING

Included Journals:EI、SCIE

Volume:379

Page Number:236-249

ISSN No.:0925-2312

Key Words:Extreme learning machine; Neighborhood rough set; Cluster analysis; Granular computing; Fuzzy set

Abstract: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.