Clustering-Based Anomaly Detection Approaches

Abstract

This chapter explores anomaly detection approaches based on explicit identification of clusters in a data set. Points that are not within a cluster become candidates to be considered anomalies. Variations among algorithms result in evaluating the relative anomalousness of points that are near (but not inside) a cluster, and also the points at the periphery of a cluster.