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 graphical tools for outlier identification. In this paper, we show that recasting FS clustering in the framework of normal mixture models can introduce some improvements: the problem of choosing a metric for clustering is avoided; membership degree is assessed by posterior probability; a testing procedure for outlier detection can be devised. © Springer-Verlag Berlin Heidelberg 2011.
CITATION STYLE
Calò, D. G. (2011). Some developments in Forward Search clustering. In Studies in Classification, Data Analysis, and Knowledge Organization (pp. 135–143). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-642-13312-1_13
Mendeley helps you to discover research relevant for your work.