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A Parallel Algorithm for Fast Mining Frequent Weighted Ultility Patterns from Dynamic Quantitative Databases

  • Nguyen Le iSPACE
  • Ham Nguyen
  • Minh Nguyen
Keywords: frequent weighted utility patterns, dynamic quantitative database, big data, parallel computation


In recent years, the problem of mining frequent weighted utility patterns has been receiving research attention. This is a variation of the pattern mining problem. Many effective methods have been proposed to solve this problem. However, when the data is extensive and the weights of items change frequently, the algorithms take much time during the mining process. If we take advantage of the parallel computing capabilities of computing systems or multi-core processors, we can improve the mining time of algorithms. This paper presents a parallel solution operating on multi-core processors, named pdFWUNL, to exploit frequent weighted utility patterns from dynamic quantitative databases with variable item
weight. The experimental results show that the mining time of our parallel algorithm, pdFWUNL, is more efficient than the best available sequential method.


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