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Outlier Detection II: Unsupervised Methods

  • Assume the normal objects are somewhat ``clustered'‘ into multiple groups, each having some distinct features
  • An outlier is expected to be far away from any groups of normal objects
  • Weakness: Cannot detect collective outlier effectively
    • Normal objects may not share any strong patterns, but the collective outliers may share high similarity in a small area
  • Ex. In some intrusion or virus detection, normal activities are diverse
    • Unsupervised methods may have a high false positive rate but still miss many real outliers.
    • Supervised methods can be more effective, e.g., identify attacking some key resources
  • Many clustering methods can be adapted for unsupervised methods
    • Find clusters, then outliers: not belonging to any cluster
    • Problem 1: Hard to distinguish noise from outliers
    • Problem 2: Costly since first clustering: but far less outliers than normal objects
      • Newer methods: tackle outliers directly

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