Data Mining and Knowledge Discovery

, Volume 31, Issue 2, pp 548–572 | Cite as

Outlier detection using binary decision diagrams

  • Takuro KutsunaEmail author
  • Akihiro Yamamoto


We propose a novel method for outlier detection using binary decision diagrams. Leave-one-out density is proposed as a new measure for detecting outliers, which is defined as a ratio of the number of data elements inside a region to the volume of the region after a focused datum is removed. We show that leave-one-out density can be evaluated very efficiently on a set of regions around each datum in a given dataset by using binary decision diagrams. The time complexity of the proposed method is nearly linear with respect to the size of the dataset, while the outlier detection accuracy is still comparable to that of other methods. Experimental results show the effectiveness of the proposed method.


Outlier detection Binary decision diagram Leave-one-out-density 


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Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.Toyota Central R&D Labs. Inc.NagakuteJapan
  2. 2.Department of Intelligence Science and Technology, Graduate School of InformaticsKyoto UniversityKyotoJapan

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