An Extremal Optimization Algorithm for Improving Gaussian Mixture Search

Rodica Lung

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Paper

An Extremal Optimization Algorithm for Improving Gaussian Mixture Search

Topics: Applications: Games and Entertainment Technologies, Evolutionary Robotics, Evolutionary Art and Design, Industrial and Real World applications, Computational Economics and Finance; Evolutionary Learning Systems

In Proceedings of the 14th International Joint Conference on Computational Intelligence - Volume 0IJCCI, 91-96, 2022 , Valletta, Malta

Author: Rodica Lung

Affiliation: Center for the Study of Complexity, Babes¸-Bolyai University, Cluj Napoca, Romania

Keyword(s): Clustering, Gaussian Mixture, Extremal Optimization.

Abstract: Many standard clustering methods rely on optimizing a maximum likelihood function to reveal internal connections within data. While relying on the same model, alternative approaches may provide better insight into the division of data. This paper presents a new Gaussian mixture clustering approach that uses an extremal optimization algorithm to maximize the silhouette coefficient. The mean and covariance matrix of each component are evolved to maximize each cluster’s priors. Numerical experiments compare the performance of the expectation-maximization algorithm with the new approach on a set of synthetic and real-world data.

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Paper citation in several formats:

Lung, R. (2022). An Extremal Optimization Algorithm for Improving Gaussian Mixture Search. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - ECTA; ISBN 978-989-758-611-8; ISSN 2184-3236, SciTePress, pages 91-96. DOI: 10.5220/0011528000003332

@conference{ecta22,
author={Rodica Lung.},
title={An Extremal Optimization Algorithm for Improving Gaussian Mixture Search},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - ECTA},
year={2022},
pages={91-96},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011528000003332},
isbn={978-989-758-611-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - ECTA
TI - An Extremal Optimization Algorithm for Improving Gaussian Mixture Search
SN - 978-989-758-611-8
IS - 2184-3236
AU - Lung, R.
PY - 2022
SP - 91
EP - 96
DO - 10.5220/0011528000003332
PB - SciTePress