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Effective and Efficient Data Retrieval System

We consider the problem of parameter estimation in statistical models in the case where data are uncertain and represented as belief functions. The proposed method is based on the maximization of a generalized likelihood criterion, which can be interpreted as a degree of agreement between the statistical model and the uncertain observations. We propose a variant of the EM algorithm that iteratively maximizes this criterion. As an illustration, the method is applied to uncertain data clustering using finite mixture models, in the cases of categorical and continuous attributes. In the existing system, User gives the Search input to the Search Engine, which provides all sets of data irrespective of Relevant Results with respect to the Query as well as Redundant Results. In the proposed system, we are using Statistical and Evidence Approach to retrieve the Results. Statistical approach is used in re-ranking the results after obtaining the Feedbacks from the different Users in the corresponding URLs. In the Evidence Approach, we are evaluating resultant URLs are really matched to the query, only then the resultant URLs are displayed to the user. Modification that we propose is to get the Feedback of Rating for both the Key word Matched data as well as Information in the Resultant Data. This Process filters unwanted Resultant and provides Exactly Matched as well as Best Resultant Data to the users.