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Fitting a Mixture Model to Three-Mode Three-Way Data with Categorical and Continuous Variables

Fitting a Mixture Model to Three-Mode Three-Way Data with Categorical and Continuous Variables The mixture likelihood approach to clustering is most often used with two-mode two-way data to cluster one of the modes (e.g., the entities) into homogeneous groups on the basis of the other mode (e.g., the attributes). In this case, the attributes can either be continuous or categorical. When the data set consists of a three-mode three-way array (e.g., attributes measured on entities in different situations), an analogous procedure is needed to enable the clustering of the entities (i.e., one of the modes) on the basis of both of the other modes simultaneously (i.e., the attributes measured in different situations). In this paper, it is shown that the finite mixture approach to clustering can be extended to analyze three-mode threeway data where some of the attributes are continuous and some are categorical. The methodology is illustrated by clustering the genotypes in a three-way soybean data set where various attributes were measured on genotypes grown in several environments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Classification Springer Journals

Fitting a Mixture Model to Three-Mode Three-Way Data with Categorical and Continuous Variables

Journal of Classification , Volume 16 (2) – Feb 28, 2014

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Publisher
Springer Journals
Copyright
Copyright © 1999 by Springer-Verlag New York Inc.
Subject
Statistics; Statistical Theory and Methods; Pattern Recognition; Bioinformatics; Signal, Image and Speech Processing; Psychometrics; Marketing
ISSN
0176-4268
eISSN
1432-1343
DOI
10.1007/s003579900057
Publisher site
See Article on Publisher Site

Abstract

The mixture likelihood approach to clustering is most often used with two-mode two-way data to cluster one of the modes (e.g., the entities) into homogeneous groups on the basis of the other mode (e.g., the attributes). In this case, the attributes can either be continuous or categorical. When the data set consists of a three-mode three-way array (e.g., attributes measured on entities in different situations), an analogous procedure is needed to enable the clustering of the entities (i.e., one of the modes) on the basis of both of the other modes simultaneously (i.e., the attributes measured in different situations). In this paper, it is shown that the finite mixture approach to clustering can be extended to analyze three-mode threeway data where some of the attributes are continuous and some are categorical. The methodology is illustrated by clustering the genotypes in a three-way soybean data set where various attributes were measured on genotypes grown in several environments.

Journal

Journal of ClassificationSpringer Journals

Published: Feb 28, 2014

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