Design and Analysis of a Reconfigurable Platform for Frequent Pattern Mining

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Provided by: Institute of Electrical & Electronic Engineers
Topic: Data Management
Format: PDF
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, the authors 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.
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