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A novel approach of mining strong jumping emerging patterns based on BSC-tree

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  • Quanzhong Liu
  • Peng Shi
  • Zhengguo Hu
  • Yang Zhang

Abstract

It is a great challenge to discover strong jumping emerging patterns (SJEPs) from a high-dimensional dataset because of the huge pattern space. In this article, we propose a dynamically growing contrast pattern tree (DGCP-tree) structure to store grown patterns and their path codes arrays with 1-bit counts, which are from the constructed bit string compression tree. A method of mining SJEPs based on DGCP-tree is developed. In order to reduce the pattern search space, we introduce a novel pattern pruning method, which dramatically reduces non-minimal jumping emerging patterns (JEPs) during the mining process. Experiments are performed on three real cancer datasets and three datasets from the University of California, Irvine machine-learning repository. Compared with the well-known CP-tree method, the results show that the proposed method is substantially faster, able to handle higher-dimensional datasets and to prune more non-minimal JEPs.

Suggested Citation

  • Quanzhong Liu & Peng Shi & Zhengguo Hu & Yang Zhang, 2014. "A novel approach of mining strong jumping emerging patterns based on BSC-tree," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(3), pages 598-615.
    • Handle: RePEc:taf:tsysxx:v:45:y:2014:i:3:p:598-615
    DOI: 10.1080/00207721.2012.724110
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    Cited by:

    1. Li, Gang & Law, Rob & Vu, Huy Quan & Rong, Jia & Zhao, Xinyuan (Roy), 2015. "Identifying emerging hotel preferences using Emerging Pattern Mining technique," Tourism Management, Elsevier, vol. 46(C), pages 311-321.

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