Current Slide

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:

Content Tools


There are currently no sources for this slide.