Abstract: Data Mining refers to extracting or mining knowledge from large amounts of data. It is also called as knowledge mining from data. Web mining is the application of data mining to extract knowledge from web data including web documents, hyperlinks between documents usage log of websites .Search engine is one of the most important applications in today’s internet. For an ambiguous query, different users may have different search targets, so the search engine doesn’t satisfy user information needs properly on the diverse aspects upon submission of same query. The computation and analysis of user search goals can be very useful in improving search engine relevance and user experience. Search history records have been clustered to discover different user search goals for a query. User click sequences are constructed from user click-through logs and can efficiently reflect the information needs of users. Virtual-documents are generated through user click sequences for clustering using clustering algorithm. We propose Cosine Similarity Algorithm to evaluate the performance of user search target computing based on restructuring web search results. Thus, we can determine the number of user search target for a query.
Keywords: Data mining, user search goals, clustering, cosine similarity algorithm.