Implementation of Web Search Result Clustering System

  • M. Hanumanthappa
  • B. R. Prakash
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 174)


Web search results clustering is an increasingly popular technique for providing useful grouping of web search results. This paper introduces a prototype web search results clustering engine that use the random sampling technique with medoids instead of centroids to improve clustering quality, Cluster labeling is achieved by combining intra-cluster and inter-cluster term extraction based on a variant of the information gain measure by using Modified Furthest Point First algorithm. M-FPF is compared against two other established web document clustering algorithms: Suffix Tree Clustering (STC) and Lingo, which are provided by the free open source Carrot2 Document Clustering Workbench. We measure cluster quality by considering precision , recall and relevance. Results from testing on different datasets show a considerable clustering quality.


Search Engine Membership Degree Document Cluster Cluster Label Suffix Tree Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Bangalore UniversityBangaloreIndia

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