A chaotic sequence-guided Harris hawks optimizer for data clustering

Autor:	Tribhuvan Singh
Rok vydání:	2020
Předmět:	0209 industrial biotechnology Sequence Computer science Chaotic Process (computing) 02 engineering and technology Domain (software engineering) Nonlinear system 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Cluster analysis Algorithm Software Statistical hypothesis testing
Zdroj:	Neural Computing and Applications. 32:17789-17803
ISSN:	1433-3058 0941-0643
DOI:	10.1007/s00521-020-04951-2
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3f882802902b1f437b1f60fc0d0088ba https://doi.org/10.1007/s00521-020-04951-2 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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