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Local Peculiarity Oriented Data Mining And Its Application In Outlier Detection

Author

Listed:
  • JIAN YANG

    (International WIC Institute, Beijing University of Technology, Beijing, China)

  • NING ZHONG

    (International WIC Institute, Beijing University of Technology, Beijing, China;
    Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi-City, Japan)

  • YIYU YAO

    (International WIC Institute, Beijing University of Technology, Beijing, China;
    Department of Computer Science, University of Regina, Regina, Canada)

  • JUE WANG

    (State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China)

Abstract

Peculiarity oriented mining (POM), aimed at discovering peculiarity rules hidden in a dataset, is a data mining method. Peculiarity factor (PF) is one of the most important concepts in POM. In this paper, it is proved that PF can accurately characterize the peculiarity of data sampled from a normal distribution. However, for a general one-dimensional distribution, it does not have the property. A local version of PF, called LPF, is proposed to solve the difficulty. LPF can effectively describe the peculiarity of data sampled from a continuous one-dimensional distribution. Based on LPF, a framework of local peculiarity oriented mining is presented, which consists of two steps, namely, peculiar data identification and peculiar data analysis. Two algorithms for peculiar data identification and a case study of peculiar data analysis are given to make the framework practical. Experiments on several benchmark datasets show their good performance.

Suggested Citation

  • Jian Yang & Ning Zhong & Yiyu Yao & Jue Wang, 2012. "Local Peculiarity Oriented Data Mining And Its Application In Outlier Detection," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 11(06), pages 1155-1181.
    • Handle: RePEc:wsi:ijitdm:v:11:y:2012:i:06:n:s0219622012500319
    DOI: 10.1142/S0219622012500319
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    Cited by:

    1. Xu, Paiheng & Zhang, Rong & Deng, Yong, 2017. "A novel weight determination method for time series data aggregation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 42-55.

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