Medoid-based shadow value validation and visualization

Budiaji, Weksi

doi:10.31227/osf.io/syekz

Cited by 1 publication

(2 citation statements)

References 7 publications

(11 reference statements)

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“…The maximum value of CH reflects a high quality of clustering solution where the ratio of to is high. The CH metric can be calculated using Equation (7).…”

Section: =1 (6) =1mentioning

confidence: 99%

“…Each cluster has a unique center that represents the minimum intraclustering distance between the centroid and all members of the cluster 6 . In clustering, the center concept can be represented as an object of the data, which is known as medoid-based clustering, or the mean of the objects located in the search space of the data, which is known as centroid-based clustering 7 . Centroid-based clustering can be represented as the mean of the objects in a cluster, where each object in the cluster has a minimum distance to the centroid compared with the other cluster centroids in the search space.…”

Section: Introductionmentioning

confidence: 99%

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Improved Firefly Algorithm with Variable Neighborhood Search for Data Clustering

Al-Behadili

2022

Baghdad Sci.J

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Among the metaheuristic algorithms, population-based algorithms are an explorative search algorithm superior to the local search algorithm in terms of exploring the search space to find globally optimal solutions. However, the primary downside of such algorithms is their low exploitative capability, which prevents the expansion of the search space neighborhood for more optimal solutions. The firefly algorithm (FA) is a population-based algorithm that has been widely used in clustering problems. However, FA is limited in terms of its premature convergence when no neighborhood search strategies are employed to improve the quality of clustering solutions in the neighborhood region and exploring the global regions in the search space. On these bases, this work aims to improve FA using variable neighborhood search (VNS) as a local search method, providing VNS the benefit of the trade-off between the exploration and exploitation abilities. The proposed FA-VNS allows fireflies to improve the clustering solutions with the ability to enhance the clustering solutions and maintain the diversity of the clustering solutions during the search process using the perturbation operators of VNS. To evaluate the performance of the algorithm, eight benchmark datasets are utilized with four well-known clustering algorithms. The comparison according to the internal and external evaluation metrics indicates that the proposed FA-VNS can produce more compact clustering solutions than the well-known clustering algorithms.

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“…The maximum value of CH reflects a high quality of clustering solution where the ratio of to is high. The CH metric can be calculated using Equation (7).…”

Section: =1 (6) =1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%