PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Framework for Mining Interesting Pattern Sets
Tijl De Bie, Kleanthis-Nikolaos Kontonasios and Eirini Spyropoulou
SIGKDD Explorations Volume 12, Number 2, 2010.

Abstract

This paper suggests a framework for mining subjectively in- teresting pattern sets that is based on two components: (1) the encoding of prior information in a model for the data miner’s state of mind; (2) the search for a pattern set that is maximally informative while efficient to convey to the data miner. We illustrate the framework with an instantiation for tile patterns in binary databases where prior information on the row and column marginals is available. This approach im- plements step (1) above by constructing the MaxEnt model with respect to the prior information [2, 3], and step (2) by relying on concepts from information and coding theory. We provide a brief overview of a number of possible ex- tensions and future research challenges, including a key chal- lenge related to the design of empirical evaluations for sub- jective interestingness measures.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7253
Deposited By:Tijl De Bie
Deposited On:14 March 2011