Current paper explores the use of multi-view learning for search result clustering. A web-snippet
can be represented using multiple views. Apart from textual view cued by both the semantic
and syntactic information, a complementary view extracted from images contained in the websnippets is also utilized in the current framework. A single consensus partitioning is finally obtained after consulting these two individual views by the deployment of a multi-objective based
clustering technique. Several objective functions including the values of a cluster quality measure evaluating the goodness of partitionings obtained using different views and an agreementdisagreement index, quantifying the amount of oneness among multiple views in generating partitionings are optimized simultaneously using AMOSA. In order to detect the number of clusters
automatically, concepts of variable length solutions and a vast range of permutation operators
are introduced in the clustering process. Finally a set of alternative partitionings are obtained on
the final Pareto front by the proposed multi-view based multi-objective technique. Experimental
results by the proposed approach on several bench-mark test datasets with respect to different
performance metrics evidently establish the power of visual and text based views in achieving
better search result clustering.