Query-specific clustering of search results based on document-context similarity scores
Edward Kai Fung Dang, Robert W. P. Luk, Dik Lun Lee, Kei Shiu Ho, Stephen C.F. Chan · 2006
This paper presents a pilot study of query-specific clustering that uses our novel document-context based similarity scores as compared with document similarity scores. Clustering is applied to the top 1000 retrieved documents for a given query. Clustering effectiveness is evaluated based on the MK1 score for TREC-2, TREC-6 and TREC-7 test collections. Encouraging results were obtained whereby document-context clustering produces better MK1 scores than document clustering with a 95% confidence level if precision and recall are equally important.