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Customizing FP-growth algorithm to parallel mining with Charm++ library
Author(s): Marek Puścian
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Paper Abstract

This paper presents a frequent item mining algorithm that was customized to handle growing data repositories. The proposed solution applies Master Slave scheme to frequent pattern growth technique. Efficient utilization of available computation units is achieved by dynamic reallocation of tasks. Conditional frequent trees are assigned to parallel workers basing on their workload. Proposed enhancements have been successfully implemented using Charm++ library. This paper discusses results of the performance of parallelized FP-growth algorithm against different datasets. The approach has been illustrated with many experiments and measurements performed using multiprocessor and multithreaded computer.

Paper Details

Date Published: 7 August 2017
PDF: 8 pages
Proc. SPIE 10445, Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017, 104452L (7 August 2017); doi: 10.1117/12.2281037
Show Author Affiliations
Marek Puścian, Warsaw Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 10445:
Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017
Ryszard S. Romaniuk; Maciej Linczuk, Editor(s)

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