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      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
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      A new approach to search result clustering and labeling

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      Author(s)
      Türel, Anıl
      Advisor
      Can, Fazlı
      Date
      2011
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      Abstract
      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.
      Keywords
      Search result clustering
      cluster labeling
      web information retrieval
      clustering evaluation
      labeling evaluation
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      http://hdl.handle.net/11693/15255
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      • Dept. of Computer Engineering - Master's degree 566
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