SlideWiki | undefined | Outlier Detection (3): Clustering-Based Methods

Small screen detected. You are viewing the mobile version of SlideWiki. If you wish to edit slides you will need to use a larger device.

Outlier Detection (3): Clustering-Based Methods

Normal data belong to large and dense clusters, whereas outliers belong to small or sparse clusters, or do not belong to any clusters

Since there are many clustering methods, there are many clustering-based outlier detection methods as well

Clustering is expensive: straightforward adaption of a clustering method for outlier detection can be costly and does not scale up well for large data sets

Example (below figure): two clusters

All points not in R form a large cluster
The two points in R form a tiny cluster, thus are outliers

Speaker notes:

There are currently no sources for this slide.