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Model-Based Clustering, Discriminant Analysis, and Density Estimation (2000)
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| Venue: | JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION |
| Citations: | 552 - 29 self |
BibTeX
@ARTICLE{Fraley00model-basedclustering,,
author = {Chris Fraley and Adrian E. Raftery},
title = {Model-Based Clustering, Discriminant Analysis, and Density Estimation},
journal = {JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION},
year = {2000},
volume = {97},
pages = {611--631}
}
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Abstract
Cluster analysis is the automated search for groups of related observations in a data set. 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 outline 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, mineeld detection, cluster recovery from noisy data, and spatial density estimation. Finally, we mention limitations of the methodology, a...
Keyphrases
discriminant analysis model-based clustering density estimation cluster analysis multivariate density estimation general methodology automated search reasonable procedure data set multivariate analysis mineeld detection clustering method noisy data spatial density estimation many cluster medical diagnosis principled statistical approach related observation cluster recovery little systematic guidance important practical question commercial software
