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Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
Class Outlier Mining
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Class Outlier Mining

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Class Outlier Mining: Distance Based Approach

Class Outlier Mining: Distance Based Approach

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  • 1. International Journal of Intelligent Technology, Vol. 2, No. 1, pp 55-68, 2007 Class Outlier Mining: Distance‐Based Approach By Nabil M. Hewahi and Motaz K. Saad Presented by Motaz K. Saad msaad@iugaza.edu Jan. 2008
  • 2. Abstract • In large datasets, identifying exception or rare cases with respect to a group of similar cases  is to be considered very significant problem.   (unusual pattern) • The traditional problem (Outlier Mining) is to  find exception or rare cases in a dataset  irrespective of the class label of these cases,  they are considered rare event with respect to  the whole dataset.  2

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