How to effectively realize the view fusion in the multi-view clustering is an important challenge. A new view fusion strategy is proposed for this task. Firstly, the partition matrix of each view is setup. And then the adaptive-fusion in each view partition is made based on the multiple view partitions by an adaptive-fusion weighting matrix. Finally, the global partition is obtained by using an integration approach. This strategy is integrated with the classic fuzzy ??-means clustering
framework, and the corresponding multi-view clustering algorithm is presented. Experimental studies are carried out on the synthetic and UCI real-world multi-view datasets. The experimental results show that the proposed algorithm outperforms several existing related algorithms in the adaptative abilities and clustering performance.