Search engine query have been paying awareness in current days. Since web contents develop, the importance of search engines became more essential and at the same instance user performance reduces. Query recommendation is a method to improve search results in web. This paper presents a method for mining search engine query logs to obtain fast query recommendation on a large scale. Search engines generally return long list of ranked pages, finding the important information related to a particular topic is becoming increasingly difficult and therefore, optimized search engines become one of the most popular solution available. In this work, an algorithm has been applied to recommend related queries to a query submitted by user. For this, the technology used for allowing query recommendations is query log which contains attributes like query name, clicked URL, rank, time. Then, the similarity based on keywords as well as clicked URL's is calculated. Additionally, clusters have been obtained by combining the similarities of both keywords and clicked URL's to perform query clustering and further the sequential order of clicked URLs in each cluster has been discovered using the modified version of an existing sequential pattern mining technique. The final by product is further optimized by re-ranking the pages using the discovered sequential patterns. The proposed system here, is based on learning from query logs that predicts user information requirements and reduces the seek time of the user within the search result list.

World Wide Web Search Engine Query Log Query Clustering Rank Updater