Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33225
Title: A transduction-based approach to fuzzy clustering, relevance ranking and cluster label generation on web search results
Authors: Matsumoto, T
Hung, E
Keywords: Fuzzy clustering
Label generation
Relevance transduction
Web search ranking
Issue Date: 2012
Publisher: Springer
Source: Journal of intelligent information systems, 2012, v. 38, no. 2, p. 419-448 How to cite?
Journal: Journal of Intelligent Information Systems 
Abstract: This paper details a modular, self-contained web search results clustering system that enhances search results by (i) performing clustering on lists of web documents returned by queries to search engines, and (ii) ranking the results and labeling the resulting clusters, by using a calculated relevance value as a degree of membership to clusters. In addition, we demonstrate an external evaluation method based on precision for comparing fuzzy clustering techniques, as well as internal measures suitable for working on non-training data. The built-in label generator uses the membership degrees and relevance values to weight the most relevant results more heavily. The membership degrees of documents to fuzzy clusters also facilitate effective detection and removal of overly similar clusters. To achieve this, our transductionbased clustering algorithm (TCA) and its fuzzy counterpart (FTCA) employ a transduction-based relevance model (TRM) to consider local relationships between each web document. Results from testing on five different real-world and synthetic datasets results show favorable results compared to established label-based clustering algorithms Suffix Tree Clustering (STC) and Lingo.
URI: http://hdl.handle.net/10397/33225
DOI: 10.1007/s10844-011-0161-8
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