Meeting 7 - Algorithms for Mining Distance-Based Outliers in Large Datasets

This week our discussion is about the VLDB'98 conference paper on "Algorithms for Mining Distance-Based Outliers in Large Datasets." by Edwin M. Knox and Raymond T. Ng of University of British Columbia.

Paper Abstract is as follows.

This paper deals with finding outliers (exceptions) in large, multidimensional datasets. The identification of outliers can 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. Existing methods that we have seen for finding outliers in large datasets can only deal efficiently with two dimensions/attributes of a dataset. Here, we study the notion of DB- (Distance- Based) outliers. While we provide formal and empirical evidence showing the usefulness of DB-outliers, we focus on the development of algorithms for computing such outliers. ( Proceedings of the 24th VLDB Conference)

Paper can be downloaded from VLDB conference website here.