Abstract: This paper proposed multiple local adaptive soft subspace clustering (LAC) ensemble (MLACE) based on multimodal perturbation．There are three merits in the proposed MLACE．Firstly,MLACE combines diversity and complement decisions generated by random initialization,parameter perturbation and feature subspace projection,so as to improve the accuracy of clustering．Secondly,the clustering ensemble information is refined.The probability of each instance belonging to all clusters is defined according to the subspace weight matrix from LAC．Thirdly,because the clustering ensemble information is refined from 0/1binary value into [0,1]real value,the consensus function in clustering ensemble can adopt real valued clustering ensemble method Fast global K means,which can further improve the accuracy of clustering ensemble．Two synthetic datasets and five UCI datasets were chosen to evaluate the accuracy of MLACE．The experiment results show that MLACE is more accurate than K-means,LAC,Multiple LAC clustering ensemble based on parameter perturbation (P-MLACE)．

Key words: Clustering ensemble,Soft subspace clustering,Local adaptive soft subspace clustering,Multimodal perturbation