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Erschienen in:
2014 | OriginalPaper | Buchkapitel
RealKrimp — Finding Hyperintervals that Compress with MDL for Real-Valued Data
verfasst von : Jouke Witteveen, Wouter Duivesteijn, Arno Knobbe, Peter Grünwald
Erschienen in: Advances in Intelligent Data Analysis XIII
Verlag: Springer International Publishing
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The MDL Principle (induction by compression) is applied with meticulous effort in the
Krimp
algorithm for the problem of itemset mining, where one seeks exceptionally frequent patterns in a binary dataset. As is the case with many algorithms in data mining,
Krimp
is not designed to cope with real-valued data, and it is not able to handle such data natively. Inspired by
Krimp
’s success at using the MDL Principle in itemset mining, we develop
RealKrimp
: an MDL-based
Krimp
-inspired mining scheme that seeks exceptionally high-density patterns in a real-valued dataset. We review how to extend the underlying Kraft inequality, which relates probabilities to codelengths, to real-valued data. Based on this extension we introduce the
RealKrimp
algorithm: an efficient method to find hyperintervals that compress the real-valued dataset, without the need for pre-algorithm data discretization.