Data Clustering Based on the Whale Optimization

Canayaz, Murat; Ozdag, Recep O.

doi:10.23884/mejt.2017.2.2.04

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…The intracluster distance function and standard deviation values showed that the whale optimization algorithm could be successfully applied in solving the clustering problem. Canayaz and Özdağ [22] obtained a feature vector called BEST in their proposed method by applying clustering with WOA. They applied clustering by calculating the Manhattan distance between this vector and the test data.…”

Section: Introductionmentioning

confidence: 99%

An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets

Mat

Inan

Karakoyun

2021

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Clustering, which is handled by many researchers, is separating data into clusters without supervision. In clustering, the data are grouped using similarities or differences between them. Many traditional and heuristic algorithms are used in clustering problems and new techniques continue to be developed today. In this study, a new and effective clustering algorithm was developed by using the Whale Optimization Algorithm (WOA) and Levy flight (LF) strategy that imitates the hunting behavior of whales. With the developed WOA-LF algorithm, clustering was performed using ten medical datasets taken from the UCI Machine Learning Repository database. The clustering performance of the WOA-LF was compared with the performance of k-means, k-medoids, fuzzy c-means and the original WOA clustering algorithms. Application results showed that WOA-LF has more successful clustering performance in general and can be used as an alternative algorithm in clustering problems.

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Section: Introductionmentioning

confidence: 99%

An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets

Mat

Inan

Karakoyun

2021

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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A comprehensive survey: Whale Optimization Algorithm and its applications

Gharehchopogh

Gholizadeh

2019

Swarm and Evolutionary Computation

516

179

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Sayisal Sinirlamali Optimizasyon İçi̇n Bali̇na Opti̇mi̇zasyon Algori̇tmasi

Karadeniz¹,

Çelik²

2020

Academic Platform Journal of Engineering and Science

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Whale Optimization Algorithm (WOA), WOA is a recently developed, nature-inspired, meta-heuristic optimization algorithm. The algorithm was developed in 2016, inspired by bubble hunting strategies used by humpback whales. To determine the performance of each optimization algorithm developed, they should be tested on a different type of optimization test problems. In this paper, we aim to investigate and analyse WOA logarithm on constrained optimization the performance and accuracy of the proposed method are examined on 13 (G1-G13) constrained numerical benchmark functions, and the obtained results are compared with other meta-heuristic optimization algorithms which taken from the literature. The experimental results show that WOA has low performance on constrained optimization.

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Data Clustering Based on the Whale Optimization

Abstract: ABSTRACT:Data clustering has emerged as a major problem in areas such

Cited by 4 publications

References 12 publications

An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets

An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets

A comprehensive survey: Whale Optimization Algorithm and its applications

Sayisal Sinirlamali Optimizasyon İçi̇n Bali̇na Opti̇mi̇zasyon Algori̇tmasi

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