The Journal of Supercomputing

, Volume 75, Issue 1, pp 170–188 | Cite as

A pattern-based outlier region detection method for two-dimensional arrays

  • Ki Yong Lee
  • Young-Kyoon SuhEmail author


Recently, with the prevalence of various sensing devices and numerical simulation software, a large amount of data is being generated in the form of a two-dimensional (2D) array. One of the important tasks for analyzing such arrays is to find anomalous or outlier regions in such a 2D array. In this article, we propose an effective method for detecting outlier regions in an arbitrary 2D array, which show a significantly different pattern from that of their surrounding regions. Unlike most existing methods that determine the outlierness of a region based on how different its average is from that of its neighboring elements, our method exploits the regression models of a region in determining its outlierness. More specifically, this method first divides the array into a number of small subarrays and then builds a regression model for each subarray. In turn, the method iteratively merges adjacent subarrays with similar regression models into larger clusters. After the clustering, the proposed method reports very small clusters as outlier regions at the final step. Lastly, we demonstrate in our experiments the effectiveness of the proposed method on synthetic and real datasets.


Outlier detection Outlier region Two-Dimensional array 



We would like to thank anonymous reviewers for their insightful comments to improve the quality of this article. We also give thanks to Sang-Un Gu for locating and preparing for the real data sets.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Division of Computer ScienceSookmyung Women’s UniversitySeoulKorea
  2. 2.School of Computer Science and EngineeringKyungpook National UniversityDaeguKorea

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