Yi Chong Gai Jin De Duo Shi Tu Ju Lei Ji Cheng Suan Fa (Improved Multi view Clustering Ensemble Algorithm)
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 usualy 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 tech nique 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.
Ji Suan Ji Ke Xue