A Novel Multiple View Clustering Using TW-k-Means Algorithm

Multiple view clustering has become an active research area in the field of data mining. However, still there is a scope for improvement in the performance of clustering. This paper proposes TW-k-means algorithm which is an automated two-level variable weighting clustering algorithm for multiview data, which can simultaneously compute weights for views and individual variables. In this algorithm, a view weight is assigned to each view to identify the compactness of the view and a variable weight is also assigned to each variable in the view to identify the importance of the variable. Both view weights and variable weights are used in the distance function to determine the clusters of objects. In the new algorithm, two additional steps are added to the iterative k-means clustering process to automatically compute the view weights and the variable weights. Experimental results show that the TW-k-means algorithm outperforms the other existing clustering algorithms in effective manner.