Lattice-based biclustering using Partition Pattern Structures

In this work we present a novel technique for exhaustive bicluster enumeration using formal concept analysis (FCA). Particularly, we use pattern structures (an extension of FCA dealing with complex data) to mine similar row/column biclusters, a specialization of biclustering when attribute values have coherent variations. We show how biclustering can benefit from the FCA framework through its robust theoretical description and efficient algorithms. Finally, we evaluate our bicluster mining approach w.r.t. a standard biclustering technique showing very good results in terms of bicluster quality and performance.