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Activity Number:
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254
- 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 30, 2018 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #329479
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Title:
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Model-Based Clustering of Time-Dependent Categorical Sequence
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Author(s):
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Yingying Zhang* and Volodymyr Melnykov
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Companies:
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The University of Alabama and University of Alabama
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Keywords:
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Clustering;
Categorical Sequences;
Time-dependent
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Abstract:
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Clustering categorical sequences is an important problem that arises in many areas such as medicine,sociology,economics,etc. It is a challenging task due to the fact that the majority of classic clustering procedures are designed for quantitative observations and there is a lack of techniques for clustering categorical data. A new method has been proposed to partition time-dependent categorical sequences. The developed methodology is illustrated on simulated and real-life datasets with promising results.
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Authors who are presenting talks have a * after their name.
