UDC 681.5

Zengyou He, Xiaofei Xu,
Department of Computer Science and Engineering, Harbin Institute of Technology, P.R. China
Joshua Zhexue Huang 2 ,
E-Business Technology Institute, The University of Hong Kong, Pokfulam, Hong Kong, P.R.China
Shengchun Deng
Department of Computer Science and Engineering, Harbin Institute of Technology, P. R. China

Abstract. 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.