Informative Clusters for Multivariate Extremes

Clustering is essential for exploratory data mining, data structure analysis and a common technique for statistical data analysis. It is widely used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics. Many clustering approaches exist with different intrinsic notions of what a cluster is. In the standard setup, the goal is to group objects into subsets, known as clusters, such that objects within a given cluster are more related to one another than the ones from a different cluster. Clustering is already quite well-known (see [4, 27] and references therein) conversely to Extreme Value Theory (EVT) which is a newer field in the machine learning community that has been used in anomaly detection [14, 28, 45, 51], classification [31, 32, 54] or clustering [10, 12, 13, 33] when dedicated to the most extreme regions of the sample space.