recently shown increasing performances motivated by the use of external resources. Following this trend, we present a new algo- rithm called Dual C-Means, which provides a theoretical back- ground for clustering in different representation spaces. Its origi- nality relies on the fact that external resources can drive the cluster- ing process as well as the labeling task in a single step. To validate our hypotheses, a series of experiments are conducted over differ- ent standard datasets and in particular over a new dataset built from the TREC Web Track 2012 to take into account query logs infor- mation. The comprehensive empirical evaluation of the proposed approach demonstrates its significant advantages over traditional clustering and labeling techniques.