We consider the problem of discovering association rules between items in a large database
of sales transactions. We present two new algorithms for solving this problem that are
fundamentally di erent from the known algorithms. Experiments with synthetic as well as real-
life data show that these algorithms outperform the known algorithms by factors ranging from
three for small problems to more than an order of magnitude for large problems. We also
show how the best features of the two proposed algorithms can be combined into a hybrid …