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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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