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Clustering-Based Method: Strength and Weakness

  • Strength
    • Detect outliers without requiring any labeled data
    • Work for many types of data
    • Clusters can be regarded as summaries of the data
    • Once the cluster are obtained, need only compare any object against the clusters to determine whether it is an outlier (fast)
  • Weakness
    • Effectiveness depends highly on the clustering method used—they may not be optimized for outlier detection
    • High computational cost: Need to first find clusters
    • A method to reduce the cost: Fixed-width clustering
      • A point is assigned to a cluster if the center of the cluster is within a pre-defined distance threshold from the point
      • If a point cannot be assigned to any existing cluster, a new cluster is created and the distance threshold may be learned from the training data under certain conditions

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