Computer Science ›› 2015, Vol. 42 ›› Issue (6): 41-45.doi: 10.11896/j.issn.1002-137X.2015.06.009

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Semi-supervised Fuzzy Clustering Ensemble Approach with Data Correlation

FENG Chen-fei, YANG Yan, WANG Hong-jun, XU Ying-ge and WANG Tao   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Semi-supervised clustering ensemble has emerged as a powerful machine learning paradigm that provides improved precision,robustness and stability by taking advantage of prior information,while most of them only consider the given pairwise constraints and do not consider the neighbors around the data points constrained in the ensemble step.In this paper,a semi-supervised fuzzy clustering ensemble with data correlation(SFCEDC)was proposed to overcome this defect.Firstly,an ensemble information matrix is built by primarily exploiting the results of semi-supervised fuzzy clustering and a similarity matrix is constructed by aggregating much information of the ensemble information matrix.And then this matrix is modified by using the given constraints and the neighbors around the data points constrained.Finally,a graph partitioning algorithm is employed to get the final clustering results.Experimental results on UCI datasets demonstrate that the proposed approach can improve clustering performance effectively.

Key words: Semi-supervised clustering ensemble,Fuzzy clustering,Pairwise constraints,Neighbors points

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