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Abstract: We examine methods for clustering in high dimensions. In the first part of the paper, we perform an experimental comparison between three batch clustering algorithms: the Expectation--Maximization (EM) algorithm, a "winner take all" version of the EM algorithm reminiscent of the K-means algorithm, and model-based hierarchical agglomerative clustering. We learn naive-Bayes models with a hidden root node, using highdimensional discrete-variable data sets (both real and synthetic). We find that... (Update)
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BibTeX entry: (Update)
M. Meila and D. Heckerman, An Experimental Comparison of Several Clustering and Initialization Methods, in: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (Morgan Kaufmann, Inc., San Francisco, CA, 1998) 386-395. http://citeseer.ist.psu.edu/meila98experimental.html More
@misc{ meila98experimental,
author = "M. Meila and D. Heckerman",
title = "An Experimental Comparison of Several Clustering and Initialization Methods",
text = "M. Meila and D. Heckerman, An Experimental Comparison of Several Clustering
and Initialization Methods, in: Proceedings of the Fourteenth Conference
on Uncertainty in Artificial Intelligence (Morgan Kaufmann, Inc., San Francisco,
CA, 1998) 386-395.",
year = "1998",
url = "citeseer.ist.psu.edu/meila98experimental.html" }
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217 Human behavior and the principle of least effort (context) - Zipf - 1949
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21 Accelerated quantification of Bayesian networks with incompl.. - Thiesson - 1995
17 An introduction to generalized linear models (context) - Dobson - 1990
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