Mining Local Staircase Patterns in Noisy Data

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

Research output: Chapter in Book/Report/Conference proceedingConference paper


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.
Original languageEnglish
Title of host publication12th IEEE International Conference on Data Mining Workshops, ICDM Workshops, Brussels, Belgium, December 10, 2012
EditorsJilles Vreeken, Charles Ling, Mohamed Javeed Zaki, Arno Siebes, Jeffrey Xu Yu, Bart Goethals, Geoffrey I. Webb, Xindong Wu
PublisherIEEE Computer Society
Publication statusPublished - 2012
EventICDM 2012: IEEE International Conference on Data Mining - Brussels, Belgium
Duration: 10 Dec 201210 Dec 2012


ConferenceICDM 2012
Abbreviated titleICDM 2012


Dive into the research topics of 'Mining Local Staircase Patterns in Noisy Data'. Together they form a unique fingerprint.

Cite this