Personalized Concept and Fuzzy Based Clustering of Search Engine Queries

Personalized search is an important research area that aims to resolve the ambiguity of query terms. Since queries submitted to search engines tend to be short and ambiguous, they are not likely to be able to express the user’s precise needs. To alleviate this problem, some search engines suggest terms that are semantically related to the submitted queries so that users can choose from the suggestions the ones that reflect their information needs. First, we develop online techniques that extract concepts from the web-snippets of the search result returned from a query and use the concepts to identify related queries for that query. A new two phase personalized agglomerative clustering algorithm and fuzzy clustering algorithm, which is able to generate personalized query clusters. Experimental results show that our approach has better precision and recall than the existing query clustering methods. So we can find an effective method for search engines to provide query suggestions on semantically related queries in order to help users formulate more effective queries to meet their diversified needs. All above analysis is done on the basis of mathematical calculations. KeywordsQuery log analysis, Clustering URL, Clustering queries, Clickthrough, Concept based clustering, Personalization, Fuzzy clustering, Search engine.