Current Slide

• Method 1: Detect outliers in the full space, e.g., HilOut Algorithm
• Find distance-based outliers, but use the ranks of distance instead of the absolute distance in outlier detection
• For each object o, find its k-nearest neighbors: nn1(o), . . . , nnk(o)
• The weight of object o:

\[ w(o)=\sum_{i=1}^{k}dist(o,nn_{i}(o)) \]

• All objects are ranked in weight-descending order
• Top-l objects in weight are output as outliers (l: user-specified parm)
• Employ space-filling curves for approximation: scalable in both time and space w.r.t. data size and dimensionality
• Method 2: Dimensionality reduction
• Works only when in lower-dimensionality, normal instances can still be distinguished from outliers
• PCA: Heuristically, the principal components with low variance are preferred because, on such dimensions, normal objects are likely close to each other and outliers often deviate from the majority