Anomaly Detection in Big Data based on Clustering

Abstract

Selection of the right tool for anomaly (outlier) detection in Big data is an urgent task. In this paper algorithms for data clustering and outlier detection that take into account the compactness and separation of clusters are provided. We consider the features of their use in this capacity. Numerical experiments on real data of different sizes demonstrate the effectiveness of the proposed algorithms.