INTERNATIONAL JOURNAL FOR DEVELOPMENT OF COMPUTER SCIENCE & TECHNOLOGY ISSN-2320-7884 (ONLINE) VOLUME-1, ISSUE-III (April-May 2013) IS NOW AVAILABLE AT: www.ijdcst.com ISSN-2321-0257 (PRINT) 112 IJDCST A New Clustering Technique based on User Search Histories Dr.J.K.R Sastry #1 , M.V.B.T Santhi #2 , S.Pavani Snigdha #3, #1 Professor, K.L.University,Vaddeswaram,Guntur(dt), #2 Assistant Professor, K.L.University,Vaddeswaram,Guntur(dt), #3 Student, K.L.University,Vaddeswaram,Guntur(dt), Abstract: Most users want their search engine to incorporate three key features in query results. Relevant results(results they are actually interested in),Uncluttered(easy to read interface),Helpful options to broaden or tighten a search for accuracy. This paper addresses the third aspect with new improvement measures for an enhanced experience to the end user. A trivial query such as travel arrangement has to be broken down into a number of co-dependent steps over a period of time based on prior search patterns of the same user thus providing customized holistic view. For instance, a user may first search on possible destinations, timeline, events, etc. After deciding when and where to go, the user may then search for the most suitable arrangements for air tickets, rental cars, lodging, meals, etc. Each step results in one or more further queries, and each query results in one or more clicks on relevant pages. Current search engines cannot support this kind of hierarchical queries. We propose to implement Random walk propagation methods that can construct user profiles based on the credentials obtained from their prior search history repositories. Combined with click points driven click graphs of user search behavior the IR system can support complex queries for future requests at reduced navigations. Random walk propagation over the query fusion graph methods support complex search quests in IR systems at reduced times. For developing an interactive IR system we also propose to use these search quests as auto complete features in similar query propagations. Biasing the ranking of search results can also be provided using ranking algorithms(top-k algorithms).Supporting these methods yields dynamic and improved performance in IR systems, by providing enriched user querying experience. A practical implementation of the proposed system validates our claim. Index Terms: query clustering, search engine, query reformulation, click graph, task identification I. INTRODUCTION AS the size and richness of information on the Web grows, so does the variety and the complexity of tasks that users try to accomplish online. Users are no longer content with issuing simple navigational queries. Various studies on query logs (e.g., Yahoo’s and AltaVista’s reveal that only about 20% of queries are navigational. The rest are informational or transactional in nature. This is because users now pursue much broader informational and task-oriented goals such as arranging for future travel, managing their finances, or planning their purchase decisions. However, the primary means of accessing information online is still through keyword queries to a search engine. To improve user’s search experience, most major commercial search engines provide query