Design and Analysis of a Reconfigurable Platform for Frequent Pattern Mining
| Authors | Song Sun and Joseph Zambreno |
|---|---|
| Type | Journal Article |
| Journal | IEEE Transactions on Parallel and Distributed Systems (TPDS) |
| Volume | 22 |
| Issue | 9 |
| Pages | 1497-1505 |
| Date | September 2011 |
Abstract
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.
BibTeX
@article{SunZam11A,
title = {Design and Analysis of a Reconfigurable Platform for Frequent Pattern Mining},
journal = {IEEE Transactions on Parallel and Distributed Systems (TPDS)},
volume = {22},
number = {9},
year = {2011},
month = sep,
pages = {1497-1505},
author = {Sun, Song and Zambreno, Joseph}
}