Link-Based Clustering for Finding Subrelevant Web Pages

We propose a new Web page clustering. Typical search engines only provide relevant pages, i.e., the pages matching users’ needs. However, we design our clustering method to provide non-relevant pages as search results when they refer to relevant pages and help users anticipate the contents of those relevant pages. We call such pages subrelevant. As it is difficult to improve Web search performance, we use subrelevancy to relax the criterion as to what kind of pages should appear in search results with the least drawback, i.e., one click away from a relevant page. Our clustering method is based on three concepts: THP, out-degree path length, and threshold parameter. We use clustering results to modify the feature vectors of Web pages. Hence, each clustering result induces a reranking of search results. We expect the reranking to raise the ranks of subrelevant pages. In the experiments with NTCIR-3 Web task test collection, our clustering largely improved the average precision by 13 percent in comparison with the baseline.