Mining association rules from relations on a parallel NCR teradata database system

Data mining from relations is becoming increasingly important with the advent of parallel database systems. In this paper, we propose a new algorithm for mining association rules from relations. The new algorithm is an enhanced version of the SETM algorithm of M. Houtsma and A. Swami (1995), and it reduces the number of candidate itemsets considerably. We implemented and evaluated the new algorithm on a parallel NCR teradata database system. The new algorithm is much faster than the SETM algorithm, and its performance is quite scalable.