AbstractSimilar to many other statistical methods, clustering approaches can fail when data dimensionality increases. This so‐called curse of dimensionality has led statisticians to develop specific models for dealing with higher dimensional data. Broadly, this review covers two frameworks for dimension reduction in model‐based clustering: methods based on variable transformation and methods based on variable selection.
