The Minimum Description Length Principle for Pattern Mining: A Survey

This is about the Minimum Description Length (MDL) principle applied to pattern mining. The length of this description is kept to the minimum. Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration, the selection of patterns constitutes a major challenge. The MDL principle, a model selection method grounded in information theory, has been applied to pattern mining with the aim to obtain compact high-quality sets of patterns. After giving an outline of relevant concepts from information theory and coding, as well as of work on the theory behind the MDL and similar principles, we review MDL-based methods for mining various types of data and patterns. Finally, we open a discussion on some issues regarding these methods, and highlight currently active related data analysis problems

RealKrimp - Finding Hyperintervals that Compress with MDL for Real-Valued Data

The MDL Principle (induction by compression) is applied with meticulous effort in the Krimpalgorithm 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, Krimpis 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 RealKrimpalgorithm: an efficient method to find hyperintervals that compress the real-valued dataset, without the need for pre-algorithm data discretization