Relational Analysis for Consensus Clustering from Multiple Partitions

This paper deals with the problem of combining multiple clustering algorithms using the same data set to get a single consensus clustering. Our contribution is to formally define the cluster consensus problem as an optimization problem. to reach this goal, we propose an original existing algorithm but still relatively unknown method named relational analysis (RA). This method has several advantages among which we can quote: its low computational complexity, it does not require a number of clusters and does not neglect the weak clustering result. The unsupervised clustering consensus method implemented in this work is quite general. We evaluate the effectiveness of cluster consensus in three qualitatively different data sets. Promising results are provided in all three situations for synthetic as well as real data sets.