Publisher Summary This chapter reviews cluster analysis and related topics or the formal
study of classification schemata, whereby objects are grouped, or clustered, according to …

The classification maximum likelihood approach is sufficiently general to encompass many
current clustering algorithms, including those based on the sum of squares criterion and on …

Deviations from theoretical assumptions together with the presence of certain amount of
outlying observations are common in many practical statistical applications. This is also the …

This paper synthesizes the results, methodology, and research conducted concerning the K‐
means clustering method over the last fifty years. The K‐means method is first introduced …

The Reviewer's Guide to Quantitative Methods in the Social Sciences is designed for
evaluators of research manuscripts and proposals in the social and behavioral sciences …

Gaussian clustering models are useful both for understanding and suggesting powerful
criteria. Banfield and Raftery, Biometriks49, 803–821 (1993), have considered a …

The number of cluster analysis techniques has increased dramatically over the last ten to
fifteen years, and they have been used in areas as distinct from one another as archaeology …

Abstract SUMMARY A parametric model for partitioning individuals into mutually exclusive
groups is given. A Bayesian analysis is applied and a loss structure imposed. A model …

Numerous papers on clustering techniques and their applications in engineering, medical,
and biological areas have appeared in pattern recognition literature during the past decade …

We introduce a new method for performing clustering with the aim of fitting clusters with
different scatters and weights. It is designed by allowing to handle a proportion α of …