On the use of Silhouette for cost based clustering

Clustering plays a fundamental role in Machine Learning. With clustering we refer to the problem of finding coherent groups in a dataset of elements. There are several algorithms to perform clustering that have been proposed in the literature, considering different costs for the optimization problems they consider. In this thesis we study the problem of clustering when the cost function is the silhouette coefficient, an index traditionally used for the internal validation of the results.