An Efficient Algorithm for Mining Association Rules in Large Databases

This paper presents an efficient algorithm for mining association rules that is fundamentally different from known algorithms and not only reduces the I/O overhead significantly but also has lower CPU overhead for most cases.

Abstract

Mining for association rules between items in a large database of sales transactions has been described as an important database mining problem. In this paper we present an efficient algorithm for mining association rules that is fundamentally different from known algorithms. Compared to the previous algorithms, our algorithm reduces both CPU and I/O overheads. In our experimental study it was found that for large databases, the CPU overhead was reduced by as much as a factor of seven and I/O was reduced by almost an order of magnitude. Hence this algorithm is especially suitable for very large size databases. The algorithm is also ideally suited for parallelization. We have performed extensive experiments and compared the performance of the algorithm with one of the best existing algorithms. 1 Introduction Increasingly, business organizations are depending on sophisticated decision-making information to maintain their competitiveness in today's demanding and fast changing marketplace...