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Design and Analysis of a Reconfigurable Platform for Frequent Pattern Mining

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2 Author(s)
Song Sun ; Dept. of Electr. & Comput. Eng., Iowa State Univ., Ames, IA, USA ; Zambreno, J.

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.

Published in:

Parallel and Distributed Systems, IEEE Transactions on  (Volume:22 ,  Issue: 9 )