Exploring the number of groups in robust model-based clustering

Abstract

Two key questions in Clustering problems are how to determine the number of groups properly and measure the strength of group-assignments. These questions are specially involved when the presence of certain fraction of outlying data is also expected. Any answer to these two key questions should depend on the assumed probabilistic- model, the allowed group scatters and what we understand by noise. With this in mind, some exploratory \trimming-based" tools are presented in this work together with their justi cations. The monitoring of optimal values reached when solving a robust clustering criteria and the use of some "discriminant" factors are the basis for these exploratory tools.