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 bi-clustering 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.