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Relational peculiarity-oriented mining

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Abstract

Peculiarity rules are a new type of useful knowledge that can be discovered by searching the relevance among peculiar data. A main task in mining such knowledge is peculiarity identification. Previous methods for finding peculiar data focus on attribute values. By extending to record-level peculiarity, this paper investigates relational peculiarity-oriented mining. Peculiarity rules are mined, and more importantly explained, in a relational mining framework. Several experiments are carried out and the results show that relational peculiarity-oriented mining is effective.

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Authors and Affiliations

  1. Department of Information Engineering, Maebashi Institute of Technology, 460-1 Kamisadori-Cho, Maebashi, 371-0816, Japan

    Muneaki Ohshima & Ning Zhong

  2. Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada, S4S 0A2

    YiYu Yao

  3. Computer Science College, Beijing University of Technology, Beijing, 100022, China

    Chunnian Liu

Authors
  1. Muneaki Ohshima
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  2. Ning Zhong
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  3. YiYu Yao
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  4. Chunnian Liu
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Correspondence to Ning Zhong.

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Ohshima, M., Zhong, N., Yao, Y. et al. Relational peculiarity-oriented mining. Data Min Knowl Disc 15, 249–273 (2007). https://doi.org/10.1007/s10618-006-0046-6

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  • DOI: https://doi.org/10.1007/s10618-006-0046-6

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