Aggregation of biclustering solutions for ensemble approach

Abstract

The biclustering methods have an increasing interest in the community of machine learning and data mining. These methods identify subsets of examples and features with interesting patterns. Recently ensemble approach has been applied to the biclustering problems with success. Their principle is to generate a set of different biclusters then aggregate them into only one. 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.