Research Article Open Access

Model for Load Balancing On Processors in Parallel Mining of Frequent Itemsets

Ravindra Patel1, S. S. Rana2 and K. R. Pardasani3
  • 1 Department of Master in Computer Applications Government Geetanjali Girl’s College, Bhopal (M.P.), India
  • 2 University Institute of Computer Science and Applications Rani Durgawati University Jabalpur (M.P.), India
  • 3 Department of Mathematics and Computer Applications Maulana Azad National Institute of Technology, Bhopal (M.P), India

Abstract

The existence of many large transactions distributed databases with high data schemas, the centralized approach for mining association rules in such databases will not be feasible. Some distributed algorithms have been developed [FDM, CD], but none of them have considered the problem of data skews in distributed mining of association rules. The skewness of datasets reduces the workload balancing between processors involved in distributed mining of association rules. It is important to invent an efficient approach for distributed mining of association rules which have the ability to generate homogeneous partitions of the whole data sets; hence the supports of most large item sets are distributed evenly across the processors. We proposed an efficient stratified sampling based partitioned technique, which generate homogeneous partitions on which processors works in parallel and generate their local concepts approximately simultaneously.

American Journal of Applied Sciences
Volume 2 No. 5, 2005, 926-931

DOI: https://doi.org/10.3844/ajassp.2005.926.931

Submitted On: 3 December 2004 Published On: 31 May 2005

How to Cite: Patel, R., Rana, S. S. & Pardasani, K. R. (2005). Model for Load Balancing On Processors in Parallel Mining of Frequent Itemsets. American Journal of Applied Sciences, 2(5), 926-931. https://doi.org/10.3844/ajassp.2005.926.931

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Keywords

  • Association rules
  • Data Mining
  • Data Skewness
  • Workload Balance
  • Parallel Mining
  • Partitioning
  • Stratified Sampling