A sampling based algorithm for finding association rules from uncertain data

Abstract

Since there are many real-life situations in which people are uncertain about the content of transactions, association rule mining with uncertain data is in demand. Most of these studies focus on the improvement of classical algorithms for frequent itemsets mining. To obtain a tradeoff between the accuracy and computation time, in this paper we introduces an efficient algorithm for finding association rules from uncertain data with sampling-SARMUT, which is based on the FAST algorithm introduced by Chen et al. Unlike FAST, SARMUT is designed for uncertain data mining. In response to the special characteristics of uncertainty, we propose a new definition of "distance" as a measure to pick representative transactions. To evaluate its performance and accuracy, a comparison against the natural extension of FAST is performed using synthetic datasets. The experimental results show that the proposed sampling algorithm SARMUT outperforms FAST algorithm, and achieves up to 97% accuracy in some cases. © 2010 Springer-Verlag.