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Record-level peculiarity-based data analysis and classifications

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Abstract

Peculiarity-oriented mining is a data mining method consisting of peculiar data identification and peculiar data analysis. Peculiarity factor and local peculiarity factor are important concepts employed to describe the peculiarity of a data point in the identification step. One can study the notions at both attribute and record levels. In this paper, a new record LPF called distance-based record LPF (D-record LPF) is proposed, which is defined as the sum of distances between a point and its nearest neighbors. The authors prove that D-record LPF can characterize the probability density of a continuous m-dimensional distribution accurately. This provides a theoretical basis for some existing distance-based anomaly detection techniques. More importantly, it also provides an effective method for describing the class-conditional probabilities in a Bayesian classifier. The result enables us to apply D-record LPF to solve classification problems. A novel algorithm called LPF-Bayes classifier and its kernelized implementation are proposed, which have some connection to the Bayesian classifier. Experimental results on several benchmark datasets demonstrate that the proposed classifiers are competitive to some excellent classifiers such as AdaBoost, support vector machines and kernel Fisher discriminant.

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Authors and Affiliations

  1. International WIC Institute, Beijing University of Technology, 100124, Beijing, China

    Jian Yang, Ning Zhong & Yiyu Yao

  2. The Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Jian Yang & Jue Wang

  3. Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan

    Ning Zhong

  4. Department of Computer Science, University of Regina, Regina, SK, Canada

    Yiyu Yao

Authors
  1. Jian Yang
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  2. Ning Zhong
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  3. Yiyu Yao
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  4. Jue Wang
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Corresponding author

Correspondence to Jian Yang.

Additional information

This paper extends and improves our previous work published in the Proceedings of the 9th IEEE International Conference on Data Mining [33].

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Yang, J., Zhong, N., Yao, Y. et al. Record-level peculiarity-based data analysis and classifications. Knowl Inf Syst 28, 149–173 (2011). https://doi.org/10.1007/s10115-010-0315-y

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  • DOI: https://doi.org/10.1007/s10115-010-0315-y

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