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Journal of Classification

Model-Based Clustering with Nested Gaussian Clusters

  • 13-11-2023
  • Original Research
Published in:
Authors
Jason Hou-Liu
Ryan P. Browne
Published in
Journal of Classification | Issue 1/2024

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Abstract

The article introduces a model-based clustering method using nested Gaussian clusters, which is particularly useful for datasets with hierarchical class labels. The proposed method extends the finite Gaussian mixture model to capture nested clustering structures, allowing for a more nuanced understanding of data variations. The model is designed to handle datasets where class labels exhibit multiple levels of hierarchy, such as cities belonging to provinces/states and nations. The article provides a detailed explanation of the model, including its specification, estimation via the expectation-maximization algorithm, and various model variations. Additionally, the article includes a simulation study and real-world dataset applications to demonstrate the model's effectiveness. The simulation study examines the performance of the model under different scenarios, while the real-world dataset applications showcase its practical applicability. The article also discusses future work, including potential improvements and extensions to the model.
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previous article Erratum: Classification Under Partial Reject Options
next article Unsupervised Classification with a Family of Parsimonious Contaminated Shifted Asymmetric Laplace Mixtures
Title
Model-Based Clustering with Nested Gaussian Clusters
Authors
Jason Hou-Liu
Ryan P. Browne
Publication date
13-11-2023
Publisher
Springer US
Published in
Journal of Classification / Issue 1/2024
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-023-09453-z
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