Some developments in forward search clustering

The Forward Search (FS) represents a useful tool for clustering data that include outlying observations, because it provides a robust clustering method in conjunction with an outlier identification method. In this paper we propose to rephrase the FS clustering process in the framework of mixture models. Through this alternative formulation, some recent theoretical results about FS-based outlier detection in multivariate normal data can be exploited and extended to the multiple cluster setting.