(Georgia Institute of Technology, 1995)
Omiecinski, Edward; Navathe, Shamkant B.; Savasere, Ashok
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.