CHARM: An Efficient Algorithm for Closed Association Rule Mining Mohammed J. Zaki Ching-Jui Hsiao The task of mining association rules consists of two main steps. The first involves finding the set of all frequent itemsets. The second step involves testing and generating all high confidence rules among itemsets. In this paper we show that it is not necessary to mine all frequent itemsets in the first step, instead it is sufficient to mine the set of closed frequent itemsets, which is much smaller than the set of all frequent itemsets. It is also not necessary to mine the set of all possible rules. We show that any rule between itemsets is equivalent to some rule between closed itemsets. Thus many redundant rules can be eliminated. Furthermore, we present CHARM, an efficient algorithm for mining all closed frequent itemsets. An extensive experimental evaluation on a number of real and synthetic databases shows that CHARM outperforms previous methods by an order of magnitude or more. It is also linearly scalable in the number of transactions and the number of closed itemsets found. Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY cs-99-10
CHARM: An Efficient Algorithm for Closed Association Rule Mining
Mohammed J. Zaki
Ching-Jui Hsiao
The task of mining association rules consists of two main steps. The first involves finding the set of all frequent itemsets. The second step involves testing and generating all high confidence rules among itemsets. In this paper we show that it is not necessary to mine all frequent itemsets in the first step, instead it is sufficient to mine the set of closed frequent itemsets, which is much smaller than the set of all frequent itemsets. It is also not necessary to mine the set of all possible rules. We show that any rule between itemsets is equivalent to some rule between closed itemsets. Thus many redundant rules can be eliminated. Furthermore, we present CHARM, an efficient algorithm for mining all closed frequent itemsets. An extensive experimental evaluation on a number of real and synthetic databases shows that CHARM outperforms previous methods by an order of magnitude or more. It is also linearly scalable in the number of transactions and the number of closed itemsets found.
Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY
cs-99-10