1. A method comprising the following computer-executable acts: accessing a data repository in a computing device, wherein the data repository comprises a plurality of queries issued by users of a search engine and sets of search results selected by issuers of respective queries in the plurality of queries; determining, for each query in the plurality of queries, a click distribution over a respective set of search results for a respective query; determining measures of similarity between queries in the plurality of queries based at least in part upon click distributions over the sets of search results for each of the respective queries, wherein a measure of similarity between a first query and a second query is a cosine similarity of click distributions over respective sets of search results for the first query and the second query, the measure of similarity computed as a function of: a first probability that a first searcher that issued the first query will select a first search result; and a second probability that a second searcher that issued the second query will select the first search result, wherein the first probability and the second probability are computed based at least in part upon the click distributions over the sets of search results for each of the respective queries; from amongst unclustered queries in the plurality of queries, selecting a query that is most often submitted to the search engine as a seed query; executing a clustering algorithm over the unclustered queries in the plurality of queries utilizing the seed query, wherein queries from the unclustered queries are placed into a cluster with the seed query if measures of similarity between the seed query and respective unclustered queries are above a threshold; labeling queries in the cluster as being similar queries; and repeating acts of selecting, executing, and labeling until there are no unclustered queries.