Search Engine Technology: Clustering Papers

Respect my authority! HITS without hyperlinks, utilizing cluster-based language models.
Authored by Lillian Lee and Oren Kurland of Cornell Univ. - 2006

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We present an approach to improving the precision of an initial document ranking wherein we utilize cluster information within a graph-based framework. The main idea is to perform re-ranking based on centrality within bipartite graphs of documents (on one side) and clusters (on the other side), on the premise that these are mutually reinforcing entities. Links between entities are created via consideration of language models induced from them. We find that our cluster-document graphs give rise to much better retrieval performance than previously proposed document-only graphs do. For example, authority-based re-ranking of documents via a HITS-style cluster-based approach outperforms a previously-proposed PageRank-inspired algorithm applied to solely-document graphs. Moreover, we also show that computing authority scores for clusters constitutes an effective method for identifying clusters containing a large percentage of relevant documents.

Related terminology: HITS, clusters, re-ranking

An Impossibility Theory for Clustering
Paper authored by Jon Kleinberg, Computer Science Professor at Cornell University. (PDF Document)

Survey of Clustering Data Mining Techniques
Exhaustive 56-page PDF document studying clustering, authored by Pavel Berkhin. (PDF Document)

Learning to Cluster Web Search Results
From MSN Search: Algorithm which clusters results with common words that have different meanings, and that indicate a different context, into relevant categories.