Computer Science ›› 2016, Vol. 43 ›› Issue (3): 256-261.doi: 10.11896/j.issn.1002-137X.2016.03.047

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Fuzzy Soft Subspace Clustering Method Based on Intelligent Optimization Algorithm

ZHANG Heng-wei, HE Jia-jing, HAN Ji-hong and WANG Jin-dong   

  • Online:2018-12-01 Published:2018-12-01

Abstract: To solve the issue of clustering on selected characteristics and the problems that fuzzy C-means is sensitive to initial value and easy to fall into local optimum,a new fuzzy subspace clustering method based on improved firefly algorithm was proposed.Based on fuzzy C-means clustering algorithm,the method uses the way to calculate feature weighting in reliability-based k-means algorithm,and combines with the global search capability of firefly algorithm to search for all the subspace.An objective function was designed to evaluate the clustering results and feature-dimension included in subspace,and it was adopted to improve the searching formula of firefly algorithm.Experimental results show that the proposed clustering method can effectively converge to the global optimal solution,and has good clustering effect and noise immunity.

Key words: Clustering analysis,Subspace clustering,Fuzzy C-mean,Firefly algorithm,Feature weighting

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