|Title||Design and Analysis of a Reconfigurable Platform for Frequent Pattern Mining|
|Publication Type||Journal Articles|
|Authors||S. Sun and J. Zambreno|
|Journal||IEEE Transactions on Parallel and Distributed Systems (TPDS)|
Frequent pattern mining algorithms are designed to find commonly occurring sets in databases. This class of algorithms is typically very memory intensive, leading to prohibitive runtimes on large databases. A class of reconfigurable architectures has been recently developed that have shown promise in accelerating some data mining applications. In this paper, we propose a new architecture for frequent pattern mining based on a systolic tree structure. The goal of this architecture is to mimic the internal memory layout of the original pattern mining software algorithm while achieving a higher throughput. We provide a detailed analysis of the area and performance requirements of our systolic tree-based architecture, and show that our reconfigurable platform is faster than the original software algorithm for mining long frequent patterns.