Optimization of a Search Engine for an Organized and Effective Browsing

In web search applications, queries are submitted to search engines to represent the information needs of users. Discovering the number of diverse user search goals for a query and depicting each goal with some keywords automatically. In the existing work propose a novel approach to infer user search goals by analyzing search engine query logs. First propose a novel approach to infer user search goals for a query by clustering our proposed feedback sessions. Second we propose a novel optimization method to map feedback sessions to pseudo-documents which can efficiently reflect user information needs. In the end, we cluster these pseudo documents to infer user search goals and depict them with some keywords. In proposed system k means clustering algorithm is computationally difficult, in order to overcome the k means clustering problem, enhancement a Fuzzy c-means clustering (FCM) algorithm to group the pseudo documents and it also measure the similarity between the pseudo terms in the documents, it improves the feedback sessions results than the normal pseudo documents. The FCM algorithm divides pseudo documents data for dissimilar size cluster by using fuzzy systems. FCM choosing cluster size and central point depend on fuzzy model. The FCM clustering algorithm it congregate quickly to a local optimum or grouping of the pseudo documents in wellorganized wayAnnotation of the search results of the database we proposed a annotation based results. We used Automatic annotation approach that first aligns the data units on a result page into different groups such that the data in the same group have the same semantic results. Finally measures the clustering results classified average precision (CAP) to evaluate the performance of the restructured web search results. Keywords-pseudo-documents, k means clustering, Fuzzy c-means clustering (FCM), annotation,classified average precision (CAP)