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* Query clustering method<ref>Wen et al. [http://portal.acm.org/ft_gateway.cfm?id=503108 "Query Clustering Using User Logs"], ''ACM TOIS, Volume 20, Issue 1, January 2002''.</ref> tries to associate related queries by clustering “session data”, which contain multiple queries and click-through information from a single user interaction. They take into account terms from result documents that a set of queries has in common. The use of query keywords together with session data is shown to be the most effective method of performing query clustering.
* Selectional preference based method<ref>Beitzel et al. [http://portal.acm.org/ft_gateway.cfm?id=1229183 "Automatic Classification of Web Queries Using Very Large Unlabeled Query Logs"], ''ACM TOIS, Volume 25, Issue 2, April 2007''.</ref> tries to exploit some [[association rules]] between the query terms to help with the query classification. Given the training data, they exploit several classification approaches including exact-match using labeled data, N-Gram match using labeled data and classifiers based on perception. They emphasize on an approach adapted from computational linguistics named selectional preferences. If x and y form a pair (x; y) and y belongs to category c, then all other pairs (x; z) headed by x belong to c. They use unlabeled query log data to mine these rules and validate the effectiveness of their approaches on some labeled queries.
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