Exploring Multiobjective Optimization for Multiview Clustering

Abstract

We present a new multiview clustering approach based on multiobjective optimization. In contrast to existing clustering algorithms based on multiobjective optimization, it is generally applicable to data represented by two or more views and does not require specifying the number of clusters a priori . The approach builds upon the search capability of a multiobjective simulated annealing based technique, AMOSA, as the underlying optimization technique. In the first version of the proposed approach, an internal cluster validity index is used to assess the quality of different partitionings obtained using different views. A new way of checking the compatibility of these different partitionings is also proposed and this is used as another objective function. A new encoding strategy and some new mutation operators are introduced. Finally, a new way of computing a consensus partitioning from multiple individual partitions obtained on multiple views is proposed. As a baseline and for comparison, two multiobjective based ensemble clustering techniques are proposed to combine the outputs of different simple clustering approaches. The efficacy of the proposed clustering methods is shown for partitioning several real-world datasets having multiple views. To show the practical usefulness of the method, we present results on web-search result clustering, where the task is to find a suitable partitioning of web snippets.