Parallel classification for data mining on shared-memory multiprocessors

Mohammed J. Zaki, Ching Tien Ho, Rakesh Agrawal

Research output: Contribution to conferencePaper

72 Citations (Scopus)

Abstract

We present parallel algorithms for building decision-tree classifiers on shared-memory multiprocessor (SMP) systems. The proposed algorithms span the gamut of data and task parallelism. The data parallelism is based on attribute scheduling among processors. This basic scheme is extended with task pipelining and dynamic load balancing to yield faster implementations. The task parallel approach uses dynamic subtree partitioning among processors. Our performance evaluation shows that the construction of a decision-tree classifier can be effectively parallelized on an SMP machine with good speedup.

Original languageEnglish
Pages198-205
Number of pages8
Publication statusPublished - 1 Jan 1999
EventProceedings of the 1999 15th International Conference on Data Engineering, ICDE-99 - Sydney, NSW, AUS
Duration: 23 Mar 199926 Mar 1999

Other

OtherProceedings of the 1999 15th International Conference on Data Engineering, ICDE-99
CitySydney, NSW, AUS
Period23/3/9926/3/99

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ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Cite this

Zaki, M. J., Ho, C. T., & Agrawal, R. (1999). Parallel classification for data mining on shared-memory multiprocessors. 198-205. Paper presented at Proceedings of the 1999 15th International Conference on Data Engineering, ICDE-99, Sydney, NSW, AUS, .