This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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636
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Type:
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Contributed
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Date/Time:
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Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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Abstract - #309259 |
Title:
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Identifying General-Shaped Clusters Using the k-Means Algorithm
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Author(s):
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Anna D. Peterson*+ and Ranjan Maitra and Arka Ghosh
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Companies:
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Iowa State University and Iowa State University and Iowa State University
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Address:
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1640 Carroll Ave, Ames, IA, 50010, USA
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Keywords:
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k-means ;
single linkage ;
clustering
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Abstract:
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The objective of clustering is to separate observations into groups such that observations within each group are similar in some sense but different from observations in other groups. We present a computationally practical non-parametric clustering approach to identify general-shaped clusters that is applicable to large datasets and multiple dimensions. Specifically, we first fit a k-means algorithm to the dataset assuming a very large number of clusters K > (presumably much larger than the true number of clusters). The advantage of using k-means is its superior computational efficiency on large datasets, however, the algorithm is designed to identify homogeneous spherically-shaped clusters in an ideal situation. We therefore identify general-shaped clusters by using a separability index to merge groups that are close together. The resulting clustering is our final derived grouping.
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