Browsing by Subject "Search result clustering"

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    ItemOpen Access
    A new approach to search result clustering and labeling
    (Springer, Berlin, Heidelberg, 2011) Türel, Anıl; Can, Fazlı
    Search 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.
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    ItemOpen Access
    A new approach to search result clustering and labeling
    (2011) Türel, Anıl
    Search engines present query results as a long ordered list of web snippets divided into several pages. Post-processing of information retrieval results for easier access to the desired information is an important research problem. A post-processing technique is clustering search results by topics and labeling these groups to reflect the topic of each cluster. In this thesis, 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 and sequential k-means clustering algorithms. Cluster labeling is crucial because meaningless or confusing labels may mislead users to check wrong clusters for the query and lose extra time. Additionally, labels should reflect the contents of documents within the cluster accurately. To be able to label clusters effectively, a new cluster labeling method based on term weighting is introduced. We also present a new metric that employs precision and recall to assess the success of cluster labeling. We adopt a comparative evaluation strategy to derive the relative performance of the proposed method with respect to the two prominent search result clustering methods: Suffix Tree Clustering and Lingo. Moreover, we perform the experiments using the publicly available Ambient and ODP-239 datasets. Experimental results show that the proposed method can successfully achieve both clustering and labeling tasks.