CLICOM: Cliques for combining multiple clusterings
Clustering has a long and rich history in a variety of scientific fields. Finding natural groupings of a data set is a hard task as attested by hundreds of clustering algorithms in the literature. Each clustering technique makes some assumptions about the underlying data set. If the assumptions hold, good clusterings can be expected. It is hard, in some cases impossible, to satisfy all the assumptions. Therefore, it is beneficial to apply different clustering methods on the same data set, or the same method with varying input parameters or both. Then, the clusterings obtained can be combined into a final clustering having better overall quality. Combining multiple clusterings into a final clustering which has better overall quality has gained significant importance recently. Our contributions are a novel method for combining a collection of clusterings into a final clustering which is based on cliques, and a novel output-sensitive clique finding algorithm which works on large and dense graphs and produces output in a short amount of time. Extensive experimental studies on real and artificial data sets demonstrate the effectiveness of our contributions.
cliques combining multiple clusterings clustering clustering ensembles maximally complete subgraphs