Mining Local Staircase Patterns in Noisy Data

Thanh Le Van, Ana Carolina Fierro, Tias Guns, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt, Kathleen Marchal

Abstract: Most traditional biclustering algorithms identify biclusters with no or little overlap. In this paper, we introduce the problem of identifying staircases of biclusters. Such staircases may be indicative for causal relationships between columns and can not easily be identified by existing biclustering algorithms. Our formalization relies on a scoring function based on the Minimum Description Length principle. Furthermore, we propose a first algorithm for identifying staircase biclusters, based on a combination of local search and constraint programming. Experiments show that the approach is promising.