Abstract: In recent years,data mining and machine learning algorithms for big data become increasingly important.In the clustering,with the appearance of multi-view data,multi-view clustering has become an important clustering method.However,many existing multi-view clustering algorithms are easily affected by parameter setting and dataset itself,so the clustering results are usually unstable.To overcome this problem,we presented a new multi-view clustering ensemble algorithm based on the multi-view K-means clustering algorithm in this paper.This algorithm uses ensemble technique to improve the multi-view K-means algorithm performance,increasing the accuracy,robustness,and stability of clustering results.It is well known that one single computer cannot process too much data,because one computer has the limited computation resources.To improve the efficiency of multi-view clustering,we implemented a distributed multi-view clustering ensemble algorithm based on distributed processing technology.Experimental results show that the proposed approach has higher efficiency when processing large dataset,and it is suitable for multi-view clustering in big data environment.

Key words: Multi-view clustering,Clustering ensemble,Distributed Computation,Parallelization