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2012 IEEE 12th International Conference on Data Mining Workshops
Mining Local Staircase Patterns in Noisy Data
Brussels, Belgium Belgium
December 10-December 10
ISBN: 978-1-4673-5164-5
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.
Index Terms:
Fault tolerance,Fault tolerant systems,Data mining,Programming,Noise,Bismuth,Encoding,biclustering,Staircase patterns,pattern sets,constraint programming,MDL
Citation:
Thanh Le Van, Ana Carolina Fierroy, Tias Guns, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt, Kathleen Marchal, "Mining Local Staircase Patterns in Noisy Data," icdmw, pp.139-146, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012
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