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Journal of Classification
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29-03-2019

Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering

Authors: Kensuke Tanioka, Hiroshi Yadohisa

Published in: Journal of Classification | Issue 1/2019

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Abstract

For multivariate categorical data, it is important to detect both clustering structures and low dimensions such that clusters are discriminated. This is because it is easy to interpret the features of clusters through the estimated low dimensions. It is sure that these existing methods for dimensional reduction clustering are useful to achieve such purpose; however, the interpretation sometimes becomes complicated due to the sign of the estimated parameters. Thus, we propose new dimensional reduction clustering with non-negativity constraints for all parameters. The proposed method has several advantages. First, when the features of clusters are interpreted, it is easier to interpret the clusters since effects of sign should not be considered. In addition, from the non-negativity and orthogonality constraints, the estimated components become perfect simple structure, which is interpretable descriptions. Second, we showed that the clustering results are not inferior to these existing methods through the simulations, although the constraints for the proposed method are strong.
previous article Comparison of Similarity Measures for Categorical Data in Hierarchical Clustering
next article Optimal Landmark Point Selection Using Clustering for Manifold Modeling and Data Classification

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Literature
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Metadata
Title
Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering
Authors
Kensuke Tanioka
Hiroshi Yadohisa
Publication date
29-03-2019
Publisher
Springer US
Published in
Journal of Classification / Issue 1/2019
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-018-9284-8

Other articles of this Issue 1/2019

Optimal Landmark Point Selection Using Clustering for Manifold Modeling and Data Classification

A Mixture of Coalesced Generalized Hyperbolic Distributions

Editorial: Journal of Classification Vol 36-1

Robustification of Gaussian Bayes Classifier by the Minimum β-Divergence Method

Comparison of Similarity Measures for Categorical Data in Hierarchical Clustering

A New Method for Classifying Random Variables Based on Support Vector Machine

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