UDC 681.5

FP-Outlier: Frequent Pattern Based Outlier Detection

Zengyou He1, Xiaofei Xu1, Joshua Zhexue Huang2 and Shengchun Deng1

  1. Department of Computer Science and Engineering, Harbin Institute of Technology
    Harbin 150001, P. R. China
  2. E-Business Technology Institute, The University of Hong Kong
    Pokfulam, Hong Kong, P.R.China
    {xiaofei, dsc}@hit.edu.cn


An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from the data set. The outliers are defined as the data transactions that contain less frequent patterns in their itemsets. We define a measure called FPOF (Frequent Pattern Outlier Factor) to detect the outlier transactions and propose the FindFPOF algorithm to discover outliers. The experimental results have shown that our approach outperformed the existing methods on identifying interesting outliers.

Publication information

Volume 2, Issue 1 (Jun 2005)
Year of Publication: 2005
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

He, Z., Xu, X., Huang, J. Z., Deng, S.: FP-Outlier: Frequent Pattern Based Outlier Detection. Computer Science and Information Systems, Vol. 2, No. 1, 103-118. (2005)