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 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 which 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 show that our approach outperformed the existing methods on identifying interesting outliers.
The High Technology Research and Development Program of China (No. 2002AA413310, No. 2003AA4Z2170, No. 2003AA413021) and the IBM SUR Research Fund supported this research.
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He, Z., Xu, X., Huang, J.Z., Deng, S. (2004). A Frequent Pattern Discovery Method for Outlier Detection. In: Li, Q., Wang, G., Feng, L. (eds) Advances in Web-Age Information Management. WAIM 2004. Lecture Notes in Computer Science, vol 3129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27772-9_80
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DOI: https://doi.org/10.1007/978-3-540-27772-9_80
Publisher Name: Springer, Berlin, Heidelberg
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