Weighting Cluster Ensembles in Evidence Accumulation Clustering

We explore the idea of evidence accumulation (EAC) for combining the results of multiple clusterings. The EAC paradigm combines the information existent in n partitions into a co-association matrix (similarity matrix) based on pairwise associations, where each partition has an identical weight in the combination process. The final data partition is obtained by applying a clustering algorithm over this co-association matrix. In this paper we propose the idea of weighting differently the partitions (WEAC). Each partition contributes differently in a weighted co-association matrix depending on the quality of the partitions, as measured by internal and relative validity indices. Based on experimental results in synthetic and real data sets, the weighting of the partitions (WEAC), generally leads to a better performance than EAC. The evaluation of results is based on a consistency index between the combined partition and the "ideal" data partition taken as ground truth