Türel, AnılCan, Fazlı2016-02-082016-02-0820110302-9743http://hdl.handle.net/11693/28246Conference name: 7th Asia Information Retrieval Societies Conference, AIRS 2011Date of Conference: December 18-20, 2011Search engines present query results as a long ordered list of web snippets divided into several pages. Post-processing of retrieval results for easier access of desired information is an important research problem. In this paper, we present a novel search result clustering approach to split the long list of documents returned by search engines into meaningfully grouped and labeled clusters. Our method emphasizes clustering quality by using cover coefficient-based and sequential k-means clustering algorithms. A cluster labeling method based on term weighting is also introduced for reflecting cluster contents. In addition, we present a new metric that employs precision and recall to assess the success of cluster labeling. We adopt a comparative strategy to derive the relative performance of the proposed method with respect to two prominent search result clustering methods: Suffix Tree Clustering and Lingo. Experimental results in the publicly available AMBIENT and ODP-239 datasets show that our method can successfully achieve both clustering and labeling tasks. © 2011 Springer-Verlag Berlin Heidelberg.EnglishCluster labelingSearch result clusteringWeb information retrievalCluster contentCluster labelingClustering approachClustering methodsClustering qualityData setsK-Means clustering algorithmPost processingPrecision and recallQuery resultsRelative performanceResearch problemsSearch resultsSuffix-treesTerm weightingWeb information retrievalClustering algorithmsContent based retrievalInformation retrievalInfrared devicesWorld Wide WebSearch enginesConference Paper10.1007/978-3-642-25631-8_2610.1007/978-3-642-25631-8