Efficient algorithms for mining outliers from large data sets

TLDR

The paper proposes a novel distance‑based outlier definition using a point’s distance to its kth nearest neighbor. The authors rank points by distance to their kth nearest neighbor, declare the top n as outliers, and introduce a partition‑based algorithm that prunes entire partitions to efficiently mine outliers, evaluated with nested‑loop, index joins, and extensive experiments. Experiments show the partition‑based algorithm yields significant computational savings and scales well with data size and dimensionality, while the NBA dataset uncovers both expected and unexpected patterns.

Abstract

In this paper, we propose a novel formulation for distance-based outliers that is based on the distance of a point from its kth nearest neighbor. We rank each point on the basis of its distance to its kth nearest neighbor and declare the top n points in this ranking to be outliers. In addition to developing relatively straightforward solutions to finding such outliers based on the classical nested-loop join and index join algorithms, we develop a highly efficient partition-based algorithm for mining outliers. This algorithm first partitions the input data set into disjoint subsets, and then prunes entire partitions as soon as it is determined that they cannot contain outliers. This results in substantial savings in computation. We present the results of an extensive experimental study on real-life and synthetic data sets. The results from a real-life NBA database highlight and reveal several expected and unexpected aspects of the database. The results from a study on synthetic data sets demonstrate that the partition-based algorithm scales well with respect to both data set size and data set dimensionality.