A Binary Factor Graph Model for Biclustering

Abstract

Abstract. Biclustering, which can be defined as the simultaneous clustering of rows and columns in a data matrix, has received increasing attention in recent years, particularly in the field of Bioinformatics (e.g. for the analysis of microarray data). This paper proposes a novel biclustering approach, which extends the Affinity Propagation [1] clustering algorithm to the biclustering case. In particular, we propose a new exemplar based model, encoded as a binary factor graph, which allows to cluster rows and columns simultaneously. Moreover, we propose a linear formulation of such model to solve the optimization problem using Linear Programming techniques. The proposed approach has been tested by using a well known synthetic microarray benchmark, with encouraging results.

Multiple Structure Recovery via Probabilistic Biclustering

“…∼100 in Gene Expression analysis versus 3-7 in this dataset). To overcome this behavior we run FABIA increasing the number of biclusters to retrieve and aggregating the results on the basis of column overlap as done in [5], this leads to an improvement of the solution quality; results are reported in Table 2.…”