Find observations that are unusually different from the others (aka anomaly detection).
Why? We may want to remove outliers, or be interested in the outliers themselves (security)
Generally does not work. It can be hard to decide when to report an outlier. There are always new ways to make outliers!
5 Types of outlier detection
- Model-based methods
- See if z-score is past a certain threshold
- Unfortunately, z-score assumes uni-modal data
- Graphical approaches
- Look at a plot, human decides if data is an outlier
- Unfortunately only in max 2-3 dimensions
- Cluster-based methods
- Cluster the data
- Find points that do not belong to clusters
- Distance-based methods
- How many points lie in a radius ?
- Global outliers
- For each point, compute the average distance to its KNN
- Outliers are points that are far from their KNNs
- Local outliers
- Outlierness ratio of example is the average distance of to its KNN over the average distance of neighbours of to their KNNs
- Supervised-learning methods
- Use supervised learning: if is an outlier, if is a regular point
- Needs supervision: we need to know what outliers look like
Local vs global outliers
It’s hard to precisely define “outliers”
- In the first case it was a “global” outlier.
- In this second case it’s a “local” outlier:
- Within normal data range, but far from other points.