A Fast Greedy Algorithm for Outlier Mining

  • Zengyou He
  • Shengchun Deng
  • Xiaofei Xu
  • Joshua Zhexue Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)


The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Recently, the problem of outlier detection in categorical data is defined as an optimization problem and a local-search heuristic based algorithm (LSA) is presented. However, as is the case with most iterative type algorithms, the LSA algorithm is still very time-consuming on very large datasets. In this paper, we present a very fast greedy algorithm for mining outliers under the same optimization model. Experimental results on real datasets and large synthetic datasets show that: (1) Our new algorithm has comparable performance with respect to those state-of-the-art outlier detection algorithms on identifying true outliers and (2) Our algorithm can be an order of magnitude faster than LSA algorithm.


Outlier Detection Hash Table Support Vector Data Description Pattern Recognition Letter Rare Classis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zengyou He
    • 1
  • Shengchun Deng
    • 1
  • Xiaofei Xu
    • 1
  • Joshua Zhexue Huang
    • 2
  1. 1.Department of Computer Science and EngineeringHarbin Institute of TechnologyChina
  2. 2.E-Business Technology InstituteThe University of Hong KongHong KongChina

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