Efficient Information Retrieval using Fuzzy Self Construction Algorithm
International Journal of Computer Applications
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
... 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.