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$$k$$-means clustering of extremes. (English) Zbl 1439.62121
Summary: The $$k$$-means clustering algorithm and its variant, the spherical $$k$$-means clustering, are among the most important and popular methods in unsupervised learning and pattern detection. In this paper, we explore how the spherical $$k$$-means algorithm can be applied in the analysis of only the extremal observations from a data set. By making use of multivariate extreme value analysis we show how it can be adopted to find “prototypes” of extremal dependence and derive a consistency result for our suggested estimator. In the special case of max-linear models we show furthermore that our procedure provides an alternative way of statistical inference for this class of models. Finally, we provide data examples which show that our method is able to find relevant patterns in extremal observations and allows us to classify extremal events.

##### MSC:
 62G32 Statistics of extreme values; tail inference 62H30 Classification and discrimination; cluster analysis (statistical aspects) 60G70 Extreme value theory; extremal stochastic processes 62M15 Inference from stochastic processes and spectral analysis
##### Software:
ElemStatLearn; transport; tailDepFun; R; texmex; maxlinearCRPS
Full Text:
##### References:
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