International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 104 - Issue 1 |
Published: October 2014 |
Authors: S.Niveditha, T.Malathi, S.R.Sivaranjhani |
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S.Niveditha, T.Malathi, S.R.Sivaranjhani . Efficient Information Retrieval using Fuzzy Self Construction Algorithm. International Journal of Computer Applications. 104, 1 (October 2014), 18-20. DOI=10.5120/18167-9031
@article{ 10.5120/18167-9031, author = { S.Niveditha,T.Malathi,S.R.Sivaranjhani }, title = { Efficient Information Retrieval using Fuzzy Self Construction Algorithm }, journal = { International Journal of Computer Applications }, year = { 2014 }, volume = { 104 }, number = { 1 }, pages = { 18-20 }, doi = { 10.5120/18167-9031 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2014 %A S.Niveditha %A T.Malathi %A S.R.Sivaranjhani %T Efficient Information Retrieval using Fuzzy Self Construction Algorithm%T %J International Journal of Computer Applications %V 104 %N 1 %P 18-20 %R 10.5120/18167-9031 %I Foundation of Computer Science (FCS), NY, USA
Different users have different search goals when they submit a query to a search engine. In this paper we aim at discovering the number of diverse user's search goal for giving a query and for each goal a keyword is associated automatically. We initially derive user's search goal for a query by clustering our proposed feedback conclave. Then the feedback conclave is mapped to pseudo-documents so that the user's needs are retrieved efficiently. Finally, these pseudo documents are then clustered to deduce user search goals and depict them with some keywords. Though K means clustering is used in the existing system sometimes queries may not exactly represent user specific information needs. This method only finds whether a pair of query is belonging to the same set of goal and does not look into goal in detail. Hence we put forward a fuzzy similarity-based self-constructing algorithm for feature clustering. Our method works efficiently and will return provide better inferred properties than any other method.