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.
Approach III: Modeling High-Dimensional Outliers
- Ex. Angle-based outliers: Kriegel, Schubert, and Zimek [KSZ08]
- For each point o, examine the angle ∆xoy for every pair of points x, y.
- Point in the center (e.g., a), the angles formed differ widely
- An outlier (e.g., c), angle variable is substantially smaller
- Use the variance of angles for a point to determine outlier
- Combine angles and distance to model outliers
- Use the distance-weighted angle variance as the outlier score
- Angle-based outlier factor (ABOF):
\[ ABOF(o)=VAR_{x,y\epsilon D,x\neq o,y\neq o}\frac{<\vec{ox},\vec{oy} > }{dist(o,x)^2dist(o,y)^2} \]
- Efficient approximation computation method is developed
- It can be generalized to handle arbitrary types of data
- Develop new models for high-dimensional outliers directly
- Avoid proximity measures and adopt new heuristics that do not deteriorate in high-dimensional data
Speaker notes:
Content Tools
Tools
Sources (0)
Tags (0)
Comments (0)
History
Usage
Questions (0)
Playlists (0)
Quality
Sources
There are currently no sources for this slide.