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Challenges of Outlier Detection

  • Modeling normal objects and outliers properly
    • Hard to enumerate all possible normal behaviors in an application
    • The border between normal and outlier objects is often a gray area
  • Application-specific outlier detection
    • Choice of distance measure among objects and the model of relationship among objects are often application-dependent
    • E.g., clinic data: a small deviation could be an outlier; while in marketing analysis, larger fluctuations
  • Handling noise in outlier detection
    • Noise may distort the normal objects and blur the distinction between normal objects and outliers. It may help hide outliers and reduce the effectiveness of outlier detection
  • Understandability
    • Understand why these are outliers: Justification of the detection
    • Specify the degree of an outlier: the unlikelihood of the object being generated by a normal mechanism

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