abstract Collaborative querying is a technique that makes use of past users' search experiences in order to help the current user formulate an appropriate query. In this technique, related queries are extracted from query logs and clustered. Queries from these clusters that are related to the user's query are then recommended to the user. This work uses a combination of query terms as well as results documents returned by queries for clustering queries. For the latter, it extracts features such as titles, URLs, and snippets from the results documents. It also proposes an extended K-means clustering algorithm for clustering queries over a simple measure of overlap. Experimental results reveal that the best clusters are obtained by using a combination of these sources rather than using only query terms or only results URLs alone.