A unified approach for mining outliers

This paper deals with nding outliers (exceptions) in large datasets. The identiication of outliers can often lead to the discovery of truly unexpected knowledge in areas such as electronic commerce, credit card fraud, and even the analysis of performance statistics of professional athletes. One contribution of this paper is to show how our proposed, intuitive notion of outliers can unify or generalize many of the existing notions of outliers provided by discordancy tests for standard statistical distributions. Thus, when mining large datasets containing many attributes, a uniied approach can replace many statistical discordancy tests, regardless of any knowledge about the underlying distribution of the attributes. A second contribution of this paper is the development of an algorithm to nd all outliers in a dataset. An important advantage of this algorithm is that its time complexity is linear with respect to the number of objects in the dataset. We include preliminary performance results.