Study of consensus functions in the context of ensemble methods for biclustering
1 : LIPADE
Université Paris V - Paris Descartes
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 aggregated them into only one. Recent works have shown that this approach could also be useful in the 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.
