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Diseño e implementación de algoritmos aproximados de clustering balanceado en PSO
(Universidad de Chile, 2012)
Este trabajo de tesis está dedicado al diseño e implementación de algoritmos aproximados que permiten explorar las mejores soluciones para el problema de Clustering Balanceado, el cual consiste en dividir un conjunto de n ...
Accelerated particle swarm optimization with explicit consideration of model constraints
(Springer, 2020-03)
Population based metaheuristic can benefit from explicit parallelization in order to address complex numerical optimization problems. Typical realistic problems usually involve non-linear functions and many constraints, ...
Interpretable interval type-2 fuzzy predicates for data clustering: A new automatic generation method based on self-organizing maps
(Elsevier Science, 2017-10)
In previous works, we proposed two methods for data clustering based on automatically discovered fuzzy predicates which were referred to as SOM-based Fuzzy Predicate Clustering (SFPC) [Meschino et al., Neurocomputing, 147, ...
Adaptación del algoritmo de clustering dinámico Pyclee para el procesamiento y análisis de trayectorias GPS
(Universidad de Guayaquil. Facultad de Ciencias Matemáticas y Físicas. Carrera de Ingeniería en Sistemas Computacionales., 2021)
La gran cantidad de datos provenientes de dispositivos GPS, motivan a los desarrolladores a descubrir el procedimiento más optimo a la hora de agrupar estas muestras de datos procedentes de geolocalización de recorridos ...
Cluster competitiveness modeling: an approach with systems dynamics
(Corporación Universidad de la Costa, 2021)
Systems analysis and improvement approach to optimize outpatient mental health treatment cascades in Mozambique (SAIA-MH): study protocol for a cluster randomized trial
(BMC_Implementation Science, 2022-06-06)
Background: Significant investments are being made to close the mental health (MH) treatment gap, which often exceeds 90% in many low- and middle-income countries (LMICs). However, limited attention has been paid to patient ...
An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning.
(Association for Computing Machinery - ACMNew York, 2014-08)
Unsupervised models can provide supplementary soft constraints to help classify new “target” data because similar instances in the target set are more likely to share the same class label. Such models can also help detect ...