Unsupervised Consensus Functions Applied to Ensemble Biclustering
Blaise Hanczar, Mohamed Nadif · 2014
The ensemble methods are very popular and can improve significantly the performance of classification and clustering algorithms. Their principle is to generate a set of different models, then aggregate them into only one. Recent works have shown that this approach can also be useful in biclustering problems.The crucial step of this approach is the consensus functions that compute the aggregation of the biclusters. We identify the main consensus functions commonly used in the clustering ensemble and show how to extend them in the biclustering context. We evaluate and analyze the performances of these consensus functions on several experiments based on both artificial and real data.