An Efficient Algorithm for Mining Association Rules in Large Databases

TLDR

Mining association rules in large sales transaction databases is a key data mining problem. The paper proposes a novel efficient algorithm for mining association rules. The algorithm was evaluated through extensive experiments comparing its performance to a leading existing algorithm. The algorithm achieves up to four‑fold CPU savings and nearly ten‑fold I/O reductions, making it especially suitable for very large databases.

Abstract

Mining for a.ssociation 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 previous algorithms, our algorithm not only reduces the I/O overhead significantly but also has lower CPU overhead for most cases. We have performed extensive experiments and compared the performance of our algorithm with one of the best existing algorithms. It was found that for large databases, the CPU overhead was reduced by as much as a factor of four and I/O was reduced by almost an order of magnitude. Hence this algorithm is especially suitable for very large size databases.