A Copula-Based Attack Prediction Model for Vehicle-to-Grid Networks

Nonvignon, Toffa Zidane; Boucif, Amar Bensaber; Mesfioui, Mhamed

doi:10.3390/app12083830

“…Copula models have been an interesting research area for decades in several areas (see, for example [23][24][25][26] ), due to Sklar's theorem 27 .…”

Section: Methodsmentioning

confidence: 99%

Novel pruning and truncating of the mixture of vine copula clustering models

Alanazi

¹

2022

Sci Rep

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The mixture of the vine copula densities allows selecting the vine structure, the most appropriate type of parametric marginal distributions, and the pair-copulas individually for each cluster. Therefore, complex hidden dependence structures can be fully uncovered and captured by the mixture of vine copula models without restriction to the parametric shape of margins or dependency patterns. However, this flexibility comes with the cost of dramatic increases in the number of model parameters as the dimension increases. Pruning and truncating each cluster of the mixture model will dramatically reduce the number of model parameters. This paper, therefore, introduced the first pruning and truncating techniques for the model-based clustering algorithm using the vine copula model, providing a significant contribution to the state-of-the-art. We apply the proposed methods to a number of well-known data sets with different dimensions. The results show that the performance of the individual pruning and truncation for each model cluster is superior to an existing vine copula clustering model.

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“…Copula models have been an interesting research area for decades in several areas (see, for example, [22][23][24][25] ), due to Sklar's theorem 26 .…”

Section: Methodsmentioning

confidence: 99%

Novel pruning and truncating of the mixture of vine copula clustering models

Alanazi

¹

2022

Preprint

0

View full text Add to dashboard Cite

The mixture of the vine copula densities allows selecting the vine structure, the most appropriate type of parametric marginal distributions, and the pair-copulas individually for each cluster. Therefore, complex hidden dependence structures can be fully uncovered and captured by the mixture of vine copula models without restriction to the parametric shape of margins or dependency patterns. However, this flexibility comes with the cost of dramatic increases in the number of model parameters as the dimension increases. Pruning and truncating each cluster of the mixture model will dramatically reduce the number of model parameters. This paper, therefore, introduced the first pruning and truncating techniques for the model-based clustering algorithm using the vine copula model, providing a significant contribution to the state-of-the-art. We apply the proposed methods to a number of well-known data sets with different dimensions. The results show that the performance of the individual pruning and truncation for each model cluster is superior to an existing vine copula clustering model.

show abstract