On Reconfigurable Association Rule Mining

As one of the most important techniques for knowledge discovery, association rule mining is known to be very computational intensive. Many hardware architectures were proposed to speed up association rule mining. Generally, theses methods are based on the usage of systolic arrays with preserved hardware resources. In this paper, we propose a reconfigurable hardware architecture which is designed to use the hardware resources more efficiently than existing methods. Explicitly, our platform can dynamically allocate cell resources according to the number of items of a candidate itemset. At the same time, our platform is able to deal with the generation of initial frequent itemsets. Note that the number of frequent item sets with fewer items is much larger than that of frequent item sets with more items. In view of this, our platform is designed to use less hardware resources to deal with frequent item sets with fewer items and reconfigure the hardware with more resources to handle frequent itemsets with more items. As such, we can process more frequent itemsets than those employing the architecture with preserved resources. In view of the growing complexity of graph mining, it has become essential to explore the approach of hardware assisted mining for better mining efficiency.