Arama sonucu kümeleme ve etiketlemeye yeni bir yaklaşım

Search engines present query results as a long ordered list of web snippets dividedinto several pages. Post-processing of information retrieval results for easier accessto the desired information is an important research problem. A post-processingtechnique is clustering search results by topics and labeling these groups to reflectthe topic of each cluster. In this thesis, we present a novel search result clusteringapproach to split the long list of documents returned by search engines intomeaningfully grouped and labeled clusters. Our method emphasizes clusteringquality by using cover coefficient and sequential k-means clustering algorithms.Cluster labeling is crucial because meaningless or confusing labels may misleadusers to check wrong clusters for the query and lose extra time. Additionally,labels should reflect the contents of documents within the cluster accurately. Tobe able to label clusters effectively, a new cluster labeling method based on termweighting is introduced. We also present a new metric that employs precision andrecall to assess the success of cluster labeling. We adopt a comparative evaluationstrategy to derive the relative performance of the proposed method with respectto the two prominent search result clustering methods: Suffix Tree Clusteringand Lingo. Moreover, we perform the experiments using the publicly availableAmbient and ODP-239 datasets. Experimental results show that the proposedmethod can successfully achieve both clustering and labeling tasks.