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A mixture of joint generalized hyperbolic distributions (MJGHD) is introduced for asymmetric clustering for high‐dimensional data. The MJGHD approach takes into account the cluster‐specific subspaces, thereby limiting the number of parameters to estimate while also facilitating visualization of results. Identifiability is discussed, and a multi‐cycle expectation–conditional maximization algorithm is outlined for parameter estimation. The MJGHD approach is illustrated on two real data sets, where the Bayesian information criterion is used for model selection. Copyright © 2018 John Wiley & Sons, Ltd.

Flexible clustering of high‐dimensional data via mixtures of joint generalized hyperbolic distributions