Arm++: A Hybrid Association Rule Mining Algorithm
Zahir Tari, Wensheng Wu Wensheng Wu · Kluwer Academic Publishers eBooks · 2006
Most of the approaches for association rule mining focus on the performance of the discovery of the frequent itemsets. They are based on the algorithms that require the transformation of data from one representation to another, and therefore excessively use resources and incur heavy CPU overhead. This chapter proposes a hybrid algorithm that is resource efficient and provides better performance. It characterizes the trade-offs among data representation, computation, I/O, and heuristics. The proposed algorithm uses an array-based item storage for the candidate and frequent itemsets. In addition, we propose a comparison algorithm (CmpApr) that compares candidate itemsets with a transaction, a filtering algorithm (FilterApr) that reduces the number of comparison operations required to find frequent itemsets. The hybrid algorithm (ARM++) integrates filtering methods within the Partition algorithm These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.