What question did this study set out to answer?

This research aims to address the gap in understanding the application of mixture models for circular response variables.

March 30, 2026Advances in Data Analysis and ClassificationOpen Access

Model-based clustering using a new mixture of circular regressions

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This research aims to address the gap in understanding the application of mixture models for circular response variables.
Developed a finite mixture of regressions model for circular data.
Utilized maximum likelihood estimation through the Expectation-Maximization algorithm.
Conducted an extensive simulation study to evaluate model performance.
Applied the model to a real dataset from a wind farm in South Africa.
Demonstrated the effectiveness of mixture models in multimodal scenarios.
Showed improved model-based clustering compared to conventional methods in circular data analysis.

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Implication

This work demonstrates finite mixture models improve clustering of circular response data, using maximum likelihood estimation in a South Africa wind farm dataset.

Cite This Study

Skhosana et al. (2026) studied this question.

synapsesocial.com/papers/69c9c5a4f8fdd13afe0bdaa4 https://doi.org/10.1007/s11634-026-00673-w

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Implication

This work demonstrates finite mixture models improve clustering of circular response data, using maximum likelihood estimation in a South Africa wind farm dataset.

Model-based clustering using a new mixture of circular regressions

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Discussion

Implication

Cite This Study

Model-based clustering using a new mixture of circular regressions

Key Points

Discussion

Implication

Cite This Study

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