Understanding the characteristics of queries wherever a search engine is failing is very important for improving search engine performance. Previous work for the most part depends on user-interaction options (e.g., click through statistics) to spot such underperforming queries. This paper evaluates the techniques used for users log history query grouping in automatic manner. Automatic query grouping is very useful for lots of software and web application. In this paper we proposes new method for calculating similarity between query using various log record attributes like time, clicked url, text similarity and frequently occurring queries using association rules. This work introduces another strong method for similar query grouping to make web browsing easy and efficient by query recommendation. A comparative evaluation of proposed method with existing work available in literature has also been carried out and the result shows that the proposed method is more effective.

Query Reformulation, Click Graph, Web Mining, Association Rules, Text Similarity

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