Hybrid clustering analysis using improved krill herd algorithm

Abualigah, Laith; Khader, Ahamad Tajudin; Hanandeh, Essam Said

doi:10.1007/s10489-018-1190-6

Cited by 316 publications

(73 citation statements)

References 65 publications

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“…Sus posibles aplicaciones son agrupamiento por similitud, predicción no lineal, aproximaciones de distribuciones multivariadas y pruebas no paramétricas de independencia entre varias variables, además de ser relativamente sencillo de implementar y computacionalmente económico [10]. Para detalles referirse a [1]. Para este experimento se usa la base de datos con las 63 observaciones en operación normal y las 31 observaciones en operación defectuosa formando una base de datos de 94 observaciones con 18 variables para el primer caso y otra usando solo las variables seleccionadas, es decir de 94 observaciones y 7 variables.…”

Section: Resultados Experimentalesunclassified

Selección óptima de variables mediante metaheurísticas binarias para la detección de fallas en un proceso industrial multivariado usando aprendizaje de máquina

Acosta¹,

Reséndiz‐Flores²,

García-Calvillo³

2019

RCS

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Resumen. Este trabajo presenta la comparación entre la optimización por enjambre de partículas y el algoritmo de búsqueda gravitacional en sus versiones binarias en combinación con la distancia Mahalanobis para la selecciónóptima de variables en un proceso de soldadura de discos de soporte para el mecanismo de los asientos en el automóvil. Los resultados muestran que la metodología implementada es capaz de seleccionar un subconjunto de variables capaz de mantener e incluso aumentar la exactitud en la detección de fallas en el proceso, así como brindar a los encargados del proceso las variables que intervienen en la variabilidad del mismo. Lo anterior se valida mediante la implementación de 16 algoritmos y enfoques de aprendizaje automático supervisado y otro no supervisado ampliamente utilizados para tareas de reconocimiento de patrones y detección de fallas.Palabras clave: metaheurísticas binarias, distancia Mahalanobis, aprendizaje automático.Abstract. This work presents the comparison between binary particle swarm optimization and binary gravitational search algorithm in combination with the Mahalanobis distance for the optimal feature selection in a welding process of support discs for the seat mechanism in the car. The results show that the implemented methodology is able to select a subset of variables capable of maintaining and even increasing the accuracy for fault detection in the process. This is validated through 37

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Section: Resultados Experimentalesunclassified

Selección óptima de variables mediante metaheurísticas binarias para la detección de fallas en un proceso industrial multivariado usando aprendizaje de máquina

Acosta¹,

Reséndiz‐Flores²,

García-Calvillo³

2019

RCS

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“…Maximal pattern mining finds the set of patterns whose super-patterns are infrequent. Closed SPM has become a research hotspot because of its impressive compression performance [46,47] and has been widely used in many essential fields, such as recommendation systems [48], clustering analysis [49][50][51], genetic engineering [52], disease diagnosis [53], and software engineering [54,55]. However, these studies ignored the repetitions that may contain more relevant information in long sequences.…”

Section: Related Workmentioning

confidence: 99%

NetNCSP: Nonoverlapping closed sequential pattern mining

Zhu

et al. 2020

Knowledge-Based Systems

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“…The cosine similarity itself is the most common method for calculating the similarity between texts that has been previously converted into vector-space model. Cosine similarity is also used in some text clustering studies for evaluating the clustering quality by calculating the similarity between the vectorized text and its cluster's centroid [18], [24], [25].…”

Section: Fitness Functionmentioning

confidence: 99%

Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

Lucky

Girsang

2020

IJCAI

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Ant colony optimization (ACO) is one of robust algorithms for solving optimization problems, including clustering. However, high and redundant computation is needed to select the proper cluster for each object, especially when the data dimensionality is high, such as social media comments. Reducing the redundant computation may cut the execution time, but it can potentially decrease the quality of clustering. With the basic idea that nearby objects tend to be in the same cluster, the nearest neighbors method can be used to choose the appropriate cluster for some objects efficiently by considering their neighbor's cluster. Therefore, this paper proposes the combination of nearest neighbors and ant colony optimization for clustering (NNACOC) which can reduce the computation time but is still able to retain the quality of clustering. To evaluate its performance, NNACOC was tested using some benchmark datasets and twitter comments. Most of the experiments show that NNACOC outperformed the original ant colony optimization for clustering (ACOC) in quality and execution time. NNACOC also yielded a better result than k-means when clustering the twitter comments. Povzetek: Predstavljen je hibridni algoritem najbližjih sosedov z optimizacijo mravljinčnih kolonij za grupiranje komentarjev socialnih medijev.

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Hybrid clustering analysis using improved krill herd algorithm

Cited by 316 publications

References 65 publications

Selección óptima de variables mediante metaheurísticas binarias para la detección de fallas en un proceso industrial multivariado usando aprendizaje de máquina

Selección óptima de variables mediante metaheurísticas binarias para la detección de fallas en un proceso industrial multivariado usando aprendizaje de máquina

NetNCSP: Nonoverlapping closed sequential pattern mining

Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

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