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article · 2005

A sampling-based framework for parallel data mining

Shengnan Cong, Jiawei Han, Jay Hoeflinger, David Padua

Cited by 42Abstract and details only

Abstract

The goal of data mining algorithm is to discover useful information embedded in large databases. Frequent itemset mining and sequential pattern mining are two important data mining problems with broad applications. Perhaps the most efficient way to solve these problems sequentially is to apply a pattern-growth algorithm, which is a divide-and-conquer algorithm [9, 10]. In this paper, we present a framework for parallel mining frequent itemsets and sequential patterns based on the divide-and-conquer strategy of pattern growth. Then, we discuss the load balancing problem and introduce a sampling technique, called selective sampling, to address this problem. We implemented parallel versions of both frequent itemsets and sequential pattern mining algorithms following our framework. The experimental results show that our parallel algorithms usually achieve excellent speedups.

Cite this paper

APA 7

Cong, S., Han, J., Hoeflinger, J., & Padua, D. (2005). A sampling-based framework for parallel data mining. https://doi.org/10.1145/1065944.1065979

MLA 9

Cong, Shengnan, et al. “A sampling-based framework for parallel data mining.” 2005, pp. 255–65, https://doi.org/10.1145/1065944.1065979.

Chicago

Cong, Shengnan, Jiawei Han, Jay Hoeflinger, and David Padua. 2005. “A sampling-based framework for parallel data mining.” https://doi.org/10.1145/1065944.1065979.

Harvard

Cong, S. et al. (2005) ‘A sampling-based framework for parallel data mining’. Available at: https://doi.org/10.1145/1065944.1065979.

BibTeX
@article{cong2005sampling,
  title = {{A sampling-based framework for parallel data mining}},
  author = {Cong, Shengnan and Han, Jiawei and Hoeflinger, Jay and Padua, David},
  pages = {255--265},
  year = {2005},
  doi = {10.1145/1065944.1065979}
}

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