Detection of Outliers in an Unsupervised Environment

  • M. Ashwini Kumari
  • M. S. Bhargavi
  • Sahana D. Gowda
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)


Outliers are exceptions when compared with the rest of the data. Outliers do not have a clear distinction with respect to regular samples in the dataset. Analysis and knowledge extraction from data with outliers lead to ambiguity and confused conclusions. Therefore, there is a need for detection of outliers as a pre-processing stage for data mining. In a multidimensional perspective, outlier detection is a challenging issue as an object may deviate in one subspace and may appear perfectly regular in another subspace. In this paper, an ensemble meta-algorithm has been proposed to analyze and vote the samples for outlier identification in multidimensional subspaces. Cook’s distance, a regression based model has been applied to detect the outliers voted by the ensemble meta-algorithm. Extensive experimentation on real datasets demonstrates the efficiency of the proposed system in detecting outliers.


Outlier detection Outlier ensemble Multidimensional subspace analysis Cook’s distance 


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Copyright information

© Springer India 2015

Authors and Affiliations

  • M. Ashwini Kumari
    • 1
  • M. S. Bhargavi
    • 1
  • Sahana D. Gowda
    • 1
  1. 1.Department of Computer Science and EngineeringBNM Institute of TechnologyBangaloreIndia

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