Relational peculiarity-oriented mining

  • Authors:
  • Muneaki Ohshima;Ning Zhong;Yiyu Yao;Chunnian Liu

  • Affiliations:
  • Department of Information Engineering, Maebashi Institute of Technology, Maebashi, Japan 371-0816;Department of Information Engineering, Maebashi Institute of Technology, Maebashi, Japan 371-0816;Department of Computer Science, University of Regina, Regina, Canada S4S 0A2;Computer Science College, Beijing University of Technology, Beijing, China 100022

  • Venue:
  • Data Mining and Knowledge Discovery
  • Year:
  • 2007

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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.