Different important studies in Web search results clustering have recently shown increasing performances motivated by the use of external resources. Following this trend, we present a new algorithm called Dual C-Means, which provides a theoretical background for clustering in different representation spaces. Its originality relies on the fact that external resources can drive the clustering process as well as the labeling task in a single step. To validate our hypotheses, a series of experiments are conducted over different standard datasets and in particular over a new dataset built from the TREC Web Track 2012 to take into account query logs information. The comprehensive empirical evaluation of the proposed approach demonstrates its significant advantages over traditional clustering and labeling techniques.
Moreno et al. REF proposed an algorithm called Dual CMeans to cluster search results in dual-representation spaces with query logs and represented the cluster label as subtopic.
Query log driven web search results clustering
Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval
Computer Science