Abstract
In this chapter, we discuss some issues arising when Gaussian Mixture Models (GMMs) are applied for clustering purposes and introduce the most commonly known strategies to solve them. Specifically, we discuss issues related to model-based clustering in high-dimensional spaces and to departures from the nominal model due to skew or contaminated data. Applications to several case studies will be presented.
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