Data analysis plays an indispensable role for understanding various phenomena. Cluster
analysis, primitive exploration with little or no prior knowledge, consists of research …

Abstract Model-based clustering is a popular tool which is renowned for its probabilistic
foundations and its flexibility. However, high-dimensional data are nowadays more and …

Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …

Cluster analysis is the automated search for groups of related observations in a dataset.
Most clustering done in practice is based largely on heuristic but intuitively reasonable …

The authors develop and estimate a conceptual model of how different aspects of customers'
relationships with the brand community influence their intentions and behaviors. The authors …

Vascular plants appeared~ 410 million years ago, then diverged into several lineages of
which only two survive: the euphyllophytes (ferns and seed plants) and the lycophytes. We …

Kaufman and Rousseeuw (1990) define cluster analysis as the classification of similar
objects into groups, in which the number of groups as well as their forms are unknown. The …

Not so long ago, multivariate analysis consisted solely of linear methods illustrated on small
to medium-sized data sets. Moreover, statistical computing meant primarily batch processing …

This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …

Recent developments in latent class (LC) analysis and associated software to include
continuous variables offer a model-based alternative to more traditional clustering …