JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2010, Vol. 40 ›› Issue (5): 56-59.

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Sampled peculiarity factor and its application in anomaly detection

SUN Jing-yu, YU Xue-li, CHEN Jun-jie, LI Xian-hua   

  1. College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China
  • Received:2010-05-20 Online:2010-10-16 Published:2010-05-20

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Abstract:

The peculiarity factor (PF), an important feature of data and obtained by accumulating differences between data, is a core concept of peculiarity-oriented mining (POM). But it meets a higher computational time complexity for any algorithm.A sampled approach firstly was suggested to define PF through analyzing current versions of PF and computational complexities of algorithms to compute it. A sampled PF (SPF) was proposed to meet realtime requirement and a SPFoutlier detection algorithm was given. Experiments using real datasets show that the SPF-outlier detection algorithm is efficient with losing a few of precisions through contrasting with two baseline algorithms and it is a feasible and right approach to define PF by sampling. Furthermore, some right sampling methods could be used to compute SPFs in order to meet real-time requirement.

Key words: sampling, peculiarity factor, outlier detection, data mining, time complexity, real-time

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