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.

Challenges of Outlier Detection

  • Modeling normal objects and outliers properly
    • Hard to enumerate all possible normal behaviors in an application
    • The border between normal and outlier objects is often a gray area
  • Application-specific outlier detection
    • Choice of distance measure among objects and the model of relationship among objects are often application-dependent
    • E.g., clinic data: a small deviation could be an outlier; while in marketing analysis, larger fluctuations
  • Handling noise in outlier detection
    • Noise may distort the normal objects and blur the distinction between normal objects and outliers. It may help hide outliers and reduce the effectiveness of outlier detection
  • Understandability
    • Understand why these are outliers: Justification of the detection
    • Specify the degree of an outlier: the unlikelihood of the object being generated by a normal mechanism

Speaker notes:

Content Tools

Sources

There are currently no sources for this slide.