Model-based clustering, discriminant analysis, and density estimation

doi:10.1198/016214502760047131

2002-06-01

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PRE

Chris Fraley

Adrian E. Raftery

DOI：10.1198/016214502760047131

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摘要

En 中文

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 procedures, and most clustering methods available in commercial software are also of this type. However, there is little systematic guidance associated with these methods for solving important practical questions that arise in cluster analysis, such as how many clusters are there, which clustering method should be used, and how should outliers be handled. We review a general methodology for model-based clustering that provides a principled statistical approach to these issues. We also show that this can be useful for other problems in multivariate analysis, such as discriminant analysis and multivariate density estimation. We give examples from medical diagnosis, minefield detection, cluster recovery from noisy data, and spatial density estimation. Finally, we mention limitations of the methodology and discuss recent developments in model-based clustering for non-Gaussian data, high-dimensional datasets, large datasets, and Bayesian estimation.

Keyword:

Bayes factor

breast cancer diagnosis

cluster analysis

EM algorithm

gene expression microarray data

Markov chain Monte Carlo

mixture model

outliers

spatial point process