concept discovery from text broad-coverage lexical resources such as wordnet are extremely useful. however, they often include many rare senses while missing domain-specific senses. we present a clustering algorithm called cbc (clustering by committee) that automatically discovers concepts from text. it initially discovers a set of tight clusters called committees that are well scattered in the similarity space. the centroid of the members of a committee is used as the feature vector of the cluster. we proceed by assigning elements to their most similar cluster. evaluating cluster quality has always been a difficult task. we present a new evaluation methodology that is based on the editing distance between output clusters and classes extracted from wordnet (the answer key). our experiments show that cbc outperforms several well-known clustering algorithms in cluster quality. mutual information (mi) is an information theoric measure and has been used in our method for clustering words.