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Activity Number: 254 - Contributed Poster Presentations: Section on Statistical Learning and Data Science
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #329479
Title: Model-Based Clustering of Time-Dependent Categorical Sequence
Author(s): Yingying Zhang* and Volodymyr Melnykov
Companies: The University of Alabama and University of Alabama
Keywords: Clustering; Categorical Sequences; Time-dependent

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.

Authors who are presenting talks have a * after their name.

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