Incomplete multi-view clustering with multiple imputation and ensemble clustering

Multi-view clustering is an important and challenging task in machine learning and data mining. In the past decade, this topic attracted much attention and there have been many progress achieved in this field. However, in reality, due to different factors such as machine error, sensor failure, multi-view data are mostly incomplete, thus how to deal with this problem becomes a challenge. Some existing works mainly deal with view missing case, which means in certain view of datasets, the whole features of some samples would be lost. In fact, missing value can occur in any position, that is, any value missing case. In that case, there would be some values missed in any view with sheerly random way. We proposed a two-stage algorithm involved multiple imputation and ensemble clustering to deal with multi-view clustering in any value missing case. Multiple imputation is adopted to deal with missing values problem and weighted ensemble clustering is applied to implement multi-view clustering. The experimental comparison on several data sets verified the effectiveness of the proposed method.