Abstract Details
Activity Number:
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245
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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Abstract - #309534 |
Title:
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Merging Mixture Components for Model-Based Clustering
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Author(s):
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Volodymyr Melnykov*+
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Companies:
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The University of Alabama
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Keywords:
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finite mixture model ;
model-based clustering ;
misclassification probability
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Abstract:
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Model-based clustering is a popular technique based on finite mixture models. It is traditionally assumed that each group of data points can be adequately modeled by a single mixture component. In this case, there exists an appealing correspondence between components and clusters. Unfortunately, more than one component might be needed for modeling a group of data. If this happens, model-based clustering loses its attractive interpretation. One possible remedy is to merge mixture components for clustering. A new merging approach based on misclassification probabilities is proposed and carefully illustrated.
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Authors who are presenting talks have a * after their name.
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