Mining related queries from search engine query logs

In this work we propose a method that retrieves a list of related queries given an initial input query. The related queries are based on the query log of previously issued queries by human users, which can be discovered using our improved association rule mining model. Users can use the suggested related queries to tune or redirect the search process. Our method not only discovers the related queries, but also ranks them according to the degree of their relatedness. Unlike many other rival techniques, it exploits only limited query log information and performs relatively better on queries in all frequency divisions