STC+ and NM-STC: Two Novel Online Results Clustering Methods for Web Searching

Abstract

Results clustering in Web Searching is useful for providing users with overviews of the results and thus allowing them to restrict their focus to the desired parts. However, the task of deriving single-word or multiple-word names for the clusters (usually referred as cluster labeling) is difficult, because they have to be syntactically correct and predictive. Moreover efficiency is an important requirement since results clustering is an online task. Suffix Tree Clustering (STC) is a clustering technique where search results (mainly snippets) can be clustered fast (in linear time), incrementally, and each cluster is labeled with a phrase. In this paper we introduce: (a) a variation of the STC, called STC+, with a scoring formula that favors phrases that occur in document titles and differs in the way base clusters are merged, and (b) a novel non merging algorithm called NM-STC that results in hierarchically organized clusters. The comparative user evaluation showed that both STC+ and NM-STC are significantly more preferred than STC, and that NM-STC is about two times faster than STC and STC+.