Abstract
We introduce two simplicial clustering approaches for compositional data, that are adaptations of the \(k\) -means and of the Gaussian mixture models algorithms, by employing the \(\alpha\) -transformation. By utilizing clustering validation indices, we can decide on the number of clusters and choose the value of \(\alpha\) for the \(k\) -means, while for the model-based clustering approach information criteria complete this task. Extensive simulation studies compare the performance of these two approaches and a real dataset illustrates their performance in real world settings.
