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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 23/2020

15.05.2020 | Original Article

A chaotic sequence-guided Harris hawks optimizer for data clustering

verfasst von: Tribhuvan Singh

Erschienen in: Neural Computing and Applications | Ausgabe 23/2020

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Abstract

Data clustering is one of the important techniques of data mining that is responsible for dividing N data objects into K clusters while minimizing the sum of intra-cluster distances and maximizing the sum of inter-cluster distances. Due to nonlinear objective function and complex search domain, optimization algorithms find difficulty during the search process. Recently, Harris hawks optimization (HHO) algorithm is proposed for solving global optimization problems. HHO has already proved its efficacy in solving a variety of complex problems. In this paper, a chaotic sequence-guided HHO (CHHO) has been proposed for data clustering. The performance of the proposed approach is compared against six state-of-the-art algorithms using 12 benchmark datasets of the UCI machine learning repository. Various comparative performance analysis and statistical tests have justified the effectiveness and competitiveness of the suggested approach.
Vorheriger Artikel Time series prediction using deep echo state networks
Nächster Artikel Correction to: Neural networks catching up with finite differences in solving partial differential equations in higher dimension

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Metadaten
Titel
A chaotic sequence-guided Harris hawks optimizer for data clustering
verfasst von
Tribhuvan Singh
Publikationsdatum
15.05.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 23/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-04951-2

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