Scuba Diver: Subspace Clustering of Web Search Results

Current search engines present their search results as a ranked list of Web pages. However, as the number of pages on the Web increases exponentially, so does the number of search results for any given query. We present a novel subspace clustering based algorithm to organize keyword search results by simultaneously clustering and identifying distinguishing terms for each cluster. Our system, named Scuba Diver, enables users to better interpret the coverage of millions of search results and to refine their search queries through a keyword guided interface. We present experimental results illustrating the effectiveness of our algorithm by measuring purity, entropy and F-measure of generated clusters based on Open Directory Project (ODP).