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Capacitated spatial clustering, a type of unsupervised machine learning method, is often used to tackle problems in compressing data, classification, logistic optimization and infrastructure optimization. Depending on the application at hand, a multitude of extensions to the clustering problem may be necessary.
Jan 4, 2024 · Traditional clustering often results in imbalanced clusters, limiting its suitability for real-world problems. In response, capacitated ...
Oct 19, 2024 · The goal is to minimize the count of opened centers while adhering to capacity constraints and achieving a satisfactory approximation of the ...
Capacitated clustering algorithms can be categorized based on data access and applications into offline, streaming, and online en- vironments. In the offline ...
In response, capacitated clustering methods have emerged, aiming to achieve balanced clusters by limiting points in each cluster. In this paper, we ...
Apr 25, 2021 · We introduce the fair-capacitated clustering problem that partitions the data into clusters of similar instances while ensuring cluster fairness and balancing ...
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Oct 18, 2022 · This paper addresses capacitated clustering based on majorization-minimization and collaborative neurodynamic optimization (CNO).
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This chapter considers the use of different capacitated clustering problems and models that fits better in real-life applications.
Jan 21, 2022 · This study proposes a new hybrid algorithm HA-CCP. In HA-CCP, a feasible solution construction method is designed to adapt to the CCP with stricter upper and ...
Oct 13, 2020 · Capacitated spatial clustering, a type of unsupervised machine learning method, is often used to tackle problems in compressing, classifying, ...
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