A parametric <Emphasis Type="Italic">k</Emphasis>-means algorithm |
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Authors: | Thaddeus Tarpey |
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Institution: | (1) Department of Mathematics and Statistics, Wright State University, Dayton, OH, USA |
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Abstract: | The k points that optimally represent a distribution (usually in terms of a squared error loss) are called the k principal points. This paper presents a computationally intensive method that automatically determines the principal points
of a parametric distribution. Cluster means from the k-means algorithm are nonparametric estimators of principal points. A parametric k-means approach is introduced for estimating principal points by running the k-means algorithm on a very large simulated data set from a distribution whose parameters are estimated using maximum likelihood.
Theoretical and simulation results are presented comparing the parametric k-means algorithm to the usual k-means algorithm and an example on determining sizes of gas masks is used to illustrate the parametric k-means algorithm. |
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Keywords: | Cluster analysis Finite mixture models Principal component analysis Principal points |
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