Efficiently Clustering Documents with Committees

Abstract

The general goal of clustering is to group data elements such that the intra-group similarities are high and the inter-group similarities are low. We present a clustering algorithm called CBC (Clustering By Committee) that is shown to produce higher quality clusters in document clustering tasks as compared to several well known clustering algorithms. It initially discovers a set of tight clusters (high intra-group similarity), called committees, that are well scattered in the similarity space (low inter-group similarity). The union of the committees is but a subset of all elements. The algorithm proceeds by assigning elements to their most similar committee. Evaluating cluster quality has always been a difficult task. We present a new evaluation methodology based on the editing distance between output clusters and manually constructed classes (the answer key). This evaluation measure is more intuitive and easier to interpret than previous evaluation measures.