Parallel data mining for association rules on shared-memory multi-processors

M. J. Zaki, M. Ogihara, S. Parthasarathy, W. Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

45 Scopus citations

Abstract

Data mining is an emerging research area, whose goal is to extract significant patterns or interesting rules from databases. High-level inference from large volumes of routine business data can provide valuable information to businesses, such as customer buying patterns, shelving criterion in supermarkets and stock trends. Many algorithms have been proposed for data mining of association rules. However, research so far has mainly focused on sequential algorithms. In this paper we present parallel algorithms for data mining of association rules, and study the degree of parallelism, synchronization, and data locality issues on the SGI Power Challenge shared-memory multi-processor. We further present a set of optimizations for the sequential and parallel algorithms. Experiments show that a significant improvement of performance is achieved using our proposed optimizations. We also achieved good speed-up for the parallel algorithm, but we observe a need for parallel I/O techniques for further performance gains.

Original languageEnglish (US)
Title of host publicationProceedings of the 1996 ACM/IEEE Conference on Supercomputing, SC 1996
PublisherAssociation for Computing Machinery
ISBN (Electronic)0897918541
DOIs
StatePublished - 1996
Event1996 ACM/IEEE Conference on Supercomputing, SC 1996 - Pittsburgh, United States
Duration: Nov 17 1996Nov 22 1996

Publication series

NameProceedings of the International Conference on Supercomputing
Volume1996-November

Conference

Conference1996 ACM/IEEE Conference on Supercomputing, SC 1996
Country/TerritoryUnited States
CityPittsburgh
Period11/17/9611/22/96

Keywords

  • Association Rules
  • Data Mining
  • Hash Tree Balancing
  • Hashing
  • Load Balancing
  • Shared-Memory Multi-processor

ASJC Scopus subject areas

  • Computer Science(all)

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