Sampling Ensembles for Frequent Patterns.

A popular solution to improving the speed and scalability of association rule mining is to do the algorithm on a random sample instead of the entire database. But it is at the expense of the accuracy of answers. In this paper, we present a sampling ensemble approach to improve the accuracy for a given sample size. Then, using Monte Carlo theory, we give an explanation for a sampling ensemble and obtain the theoretically low bound of sample size to ensure the feasibility and validity of an ensemble. And for learning the origination of the sample error and therefore giving theoretical guidance for obtaining more accurate answers, bias-variance decomposition is used in analyzing the sample error of an ensemble. According to theoretical analysis and real experiments, we conclude that sampling ensemble method can not only significantly improve the accuracy of answers, but also be a new means to solve the difficulty of determining appropriate sample size needed. [ABSTRACT FROM AUTHOR]