Fast Algorithms For Mining Association Rules In Datamining
P. Usharani · International journal of scientific and technology research · 2013
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 algo-rithms. Empirical evaluation shows that these algorithms outperform the known algorithms by factors ranging from three for small problems to more than an order of mag-nitude for large problems. We also show how the best features of the two proposed algorithms can be combined into a hybrid algorithm, called AprioriHybrid. Scale-up experiments show that AprioriHybrid scales linearly with the number of transactions. AprioriHybrid also has ex-cellent scale-up properties with respect to the transaction size and the number of items in the database. ————————————————————