Detecting Outliers in High-Dimensional Datasets with Mixed Attributes

Outlier Detection has attracted substantial attention in many applications and research areas. Examples include detection of network intrusions or credit card fraud. Many of the existing approaches are based on pair-wise distances among all points in the dataset. These approaches cannot easily extend to current datasets that usually contain a mix of categorical and continuous attributes, and may be scattered over large geographical areas. In addition, current datasets usually have a large number of dimensions. These datasets tend to be sparse, and traditional concepts such as Euclidean distance or nearest neighbor become unsuitable. We propose ODMAD, a fast outlier detection strategy intended for datasets containing mixed attributes. ODMAD takes into consideration the sparseness of the dataset, and is experimentally shown to be highly scalable with the number of points and number of attributes in the dataset.