Efficient Removal of Weak Associations in Consensus Clustering

Consensus clustering methods measure the strength of an association between two data objects based on how often the objects are grouped together by the base clusterings. However, incorporating weak associations in the consensus process can have a negative effect on the quality of the aggregated clustering. This paper presents an efficient automatic approach for removing weak associations during the consensus process. We compare our approach to a brute force method used in an existing consensus function, NegMM, which tends to be rather inefficient in terms of runtime. Our empirical analysis on multiple datasets shows that the proposed approach produces consensus clusterings that are comparable in quality to the ones produced by the original NegMM method, yet at a much lower computational cost.