PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Direct Local Pattern Sampling by Efficient Two-Step Random Procedures
Mario Boley, Claudio Lucchese, Daniel Paurat and Thomas Gaertner
In: SIGKDD 2011, USA(2011).

Abstract

We present several exact and highly scalable local pattern sampling algorithms. They can be used as an alternative to exhaustive local pattern discovery methods (e.g, frequent set mining or optimistic-estimator-based subgroup discovery) and can substantially improve efficiency as well as controllability of pattern discovery processes. While previous sampling approaches mainly rely on the Markov chain Monte Carlo method, our procedures are direct, i.e., non process-simulating, sampling algorithms. The advantages of these direct methods are an almost optimal time complexity per pattern as well as an exactly controlled distribution of the produced patterns. Namely, the proposed algorithms can sample (item-)sets according to frequency, area, squared frequency, and a class discriminativity measure. Experiments demonstrate that these procedures can improve the accuracy of pattern-based models similar to frequent sets and often also lead to substantial gains in terms of scalability.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:9317
Deposited By:Thomas Gaertner
Deposited On:16 March 2012