An Eecient Algorithm for Mining Association Rules in Large Databases

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 eecient algorithm for mining association rules that is fundamentally diierent 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 Figure or Table

Statistics

0102030'97'99'01'03'05'07'09'11'13'15'17
Citations per Year

196 Citations

Semantic Scholar estimates that this publication has 196 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Savasere1995AnEA, title={An Eecient Algorithm for Mining Association Rules in Large Databases}, author={Ashok Savasere and Edward Omiecinski and Shamkant B. Navathe}, year={1995} }