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A Platform for Parallel Data Mining on Cluster System

In: Current Trends in High Performance Computing and Its Applications

Author

Listed:
  • Shaochun Wu

    (Shanghai University, School of Computer Engineering and Science)

  • Gengfeng Wu

    (Shanghai University, School of Computer Engineering and Science)

  • Zhaochun Yu

    (Shanghai University, School of Computer Engineering and Science)

  • Hua Ban

    (Shanghai University, School of Computer Engineering and Science)

Abstract

This paper presents a Parallel Data Mining Platform (PDMP), aiming at rapidly developing parallel data mining applications on cluster system. This platform consists of parallel data mining algorithm library, data warehouse, field knowledge base and platform middleware. The middleware is the kernel of the platform, which comprises data processing component, task manager, data manager, GUI, and so on. Taking advantage of cluster system, the middleware provides a convenient developing environment and effective bottom supports for implementation of parallel data mining algorithms. So far, the parallel data mining algorithm library has possessed several parallel algorithms, such as classification, clustering, association rule mining, sequence pattern mining and so on. With a register mechanism, the parallel data mining algorithm library is easy to be extended by users. Experiment results with the use of the PDMP show that there is a substantial performance improvement due to the cooperation of the middleware and corresponding parallel strategy.

Suggested Citation

  • Shaochun Wu & Gengfeng Wu & Zhaochun Yu & Hua Ban, 2005. "A Platform for Parallel Data Mining on Cluster System," Springer Books, in: Wu Zhang & Weiqin Tong & Zhangxin Chen & Roland Glowinski (ed.), Current Trends in High Performance Computing and Its Applications, pages 155-164, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-27912-9_15
    DOI: 10.1007/3-540-27912-1_15
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