CBAR: an efficient method for mining association rules

摘要

The discovery of association rules is an important data-mining task for which many algorithms have been proposed. However, the efficiency of these algorithms needs to be improved to handle real-world large datasets. In this paper, we present an efficient algorithm named cluster-based association rule (CBAR). The CBAR method is to create cluster tables by scanning the database once, and then clustering the transaction records to the k-th cluster table, where the length of a record is k. Moreover, the large itemsets are generated by contrasts with the partial cluster tables. This not only prunes considerable amounts of data reducing the time needed to perform data scans and requiring less contrast, but also ensures the correctness of the mined results. Experiments with the FoodMart transaction database provided by Microsoft SQL Server show that CBAR outperforms Apriori, a well-known and widely used association rule.