Hybrid distance functions for K-Means clustering algorithms

Dalatu, Paul Inuwa; Fitrianto, Anwar

doi:10.3233/sji-160285

Cited by 6 publications

(7 citation statements)

References 24 publications

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“…Data clustering [1] is an emerging and dynamic area of data mining research with active interest due to its importance in a variety of fields of research, including data mining, statistics, spatial database technology, machine learning, web search, information retrieval, biology, marketing, and others. Clustering is a type of observation-based learning rather than learning by examples because it is an unsupervised approach to classification that lacks labeled information.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Agent K-Means Algorithm for Improved Parallel Data Clustering

Park¹,

Mostafa

Baharum

et al. 2022

JOIV : Int. J. Inform. Visualization

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Due to the rapid increase in data volumes, clustering algorithms are now finding applications in a variety of fields. However, existing clustering techniques have been deemed unsuccessful in managing large data volumes due to the issues of accuracy and high computational cost. As a result, this work offers a parallel clustering technique based on a combination of the K-means and Multi-Agent System algorithms (MAS). The proposed technique is known as Multi-K-means (MK-means). The main goal is to keep the dataset intact while boosting the accuracy of the clustering procedure. The cluster centers of each partition are calculated, combined, and then clustered. The performance of the suggested method's statistical significance was confirmed using the five datasets that served as testing and assessment methods for the proposed algorithm's efficacy. In terms of performance, the proposed MK-means algorithm is compared to the Clustering-based Genetic Algorithm (CGA), the Adaptive Biogeography Clustering-based Genetic Algorithm (ABCGA), and standard K-means algorithms. The results show that the MK-means algorithm outperforms other algorithms because it works by activating agents separately for clustering processes while each agent considers a separate group of features.

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Section: Introductionmentioning

confidence: 99%

A Multi-Agent K-Means Algorithm for Improved Parallel Data Clustering

Park¹,

Mostafa

Baharum

et al. 2022

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…El agrupamiento de datos es un área única y dinámica, que se ha convertido recientemente en un nicho de investigación muy activo para la minería de datos. Sirve también como el punto focal para estadísticas, aprendizaje automático, tecnología de bases de datos espaciales, recuperación de información, entre otros campos de aplicación [Flores-Cruz et al, 2017], [Dalatu et al, 2017].…”

Section: Procesos De Agrupamientounclassified

“…Un grupo apropiado se logra cuando los elementos de éste están altamente interrelacionados y son diferentes de los elementos en otros grupos. [Dalatu et al, 2017].…”

Section: Procesos De Agrupamientounclassified

“…Para lograr lo anterior, el algoritmo selecciona k centros al azar basándose en los objetos, por lo que cada uno representa una media o centroide inicial del grupo, luego, para los objetos restantes, asigna cada objeto al grupo con el que se relaciona mejor, establecido por la distancia entre el objeto y la media del grupo, después calcula una nueva media para cada grupo utilizando los objetos asignados al grupo en la iteración anterior. [Dalatu et al, 2017].…”

Section: K-mediasunclassified

“…La distancia híbrida podría implementarse de diferentes maneras, por ejemplo, Dalatu [Dalatu et al, 2017] propone utilizar la distancia Manhattan y la distancia Chebyshev, que por definición es el promedio de ambas distancias aplicadas a cada par de puntos.…”

Section: Distancia Híbridaunclassified

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