Lambda Consensus Clustering

Abstract

This paper introduces an extension to consensus clustering that allows a feedback of the results of the consensus to the original clustering processes. The original clustering processes may use this information to update their partitioning of the data. An exponential weighting approach, called lambda consensus, is presented as a method to merged the consensus information into graph based and vector space based clustering algorithms. Successful consensus clustering is highly dependent on the quality and diversity of the partitions in the ensemble. The feedback signal allows the clustering processes to adapt their algorithms to attempt to improve quality and diversity of the set of partitions in the ensemble. Communication requirements are on the same order as consensus clustering as only the consensus labels are returned to the clustering processes. The method is evaluated on real world data sets.