Multiview clustering algorithms allow leveraging information from multiple views of the data and therefore lead to improved clustering. A number of kernel based multiview clustering algorithms work by using the kernel matrices defined on the different views of the data. However, these algorithms assume availability of features from all the views of each example, i.e., assume that the kernel matrix for each view is complete. We present an approach that allows these algorithms to be applicable even when only one (the primary) view is complete and the auxiliary views are incomplete (i.e., features from these views are available only for some of the examples). Taking the kernel CCA based multiview clustering as an example, we apply our method on webpage clustering with multiple views of the data where one view is the page-text and other view is the social tags assigned to the webpage. We consider the case when the tags are available only for a small subset of the webpages which means that the tag view is incomplete. Experimental results establish the effectiveness of the proposed method.