Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence accumulation clustering (EAC) paradigm, the clustering ensemble is transformed into a pairwise co-association matrix, thus avoiding the label correspondence problem, which is intrinsic to other clustering ensemble schemes. In this paper, we propose a consensus clustering approach based on the EAC paradigm, which is not limited to crisp partitions and fully exploits the nature of the co-association matrix. Our solution determines probabilistic assignments of data points to clusters by minimizing a Bregman divergence between the observed co-association frequencies and the corresponding co-occurrence probabilities expressed as functions of the unknown assignments. We additionally propose an optimization algorithm to find a solution under any double-convex Bregman divergence. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach.
- Bregman divergence
- consensus clustering
- ensemble clustering
- evidence accumulation
Lourenço, A., Rota Bulò, S., Rebagliati, N., Fred, A. L. N., Figueiredo, M. A. T., & Pelillo, M. (2013). Probabilistic consensus clustering using evidence accumulation. Machine Learning, 98(1-2), 331-357. https://doi.org/10.1007/s10994-013-5339-6