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Activity Number:
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175
- Contributed Poster Presentations: Section on Statistical Learning and Data Science
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Type:
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Contributed
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Date/Time:
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Monday, July 31, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #323229
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Title:
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Model-Based Clustering with Application of Copula for Symbolic Data
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Author(s):
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Wenhao Pan* and lynne Billard
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Companies:
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University of Georgia and University of Georgia
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Keywords:
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Symbolic Data ;
Model-based Clustering ;
Copulas
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Abstract:
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Contemporary data sets can be too large or complex for traditional statistical methods to handle. One approach is to use symbolic data first introduced by Diday (1987). Our interest is the study of model-based clustering for symbolic data, especially for distributions (i.e., observations are not the single numerical point values). We will describe symbolic data and consider differences between symbolic data and classical data. When we only have the marginal distributions, we do not know the dependence relationship between random variables. One approach to measure these dependences is that of Vrac et al. (2012) in which a copula function is used to describe the cumulative joint distribution function of random variables in a mixture model. We will review these ideas.
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Authors who are presenting talks have a * after their name.
