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A chaotic sequence-guided Harris hawks optimizer for data clustering.

Authors :
Singh, Tribhuvan
Source :
Neural Computing & Applications; Dec2020, Vol. 32 Issue 23, p17789-17803, 15p
Publication Year :
2020

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09410643
Volume :
32
Issue :
23
Database :
Complementary Index
Journal :
Neural Computing & Applications
Publication Type :
Academic Journal
Accession number :
146996814
Full Text :
https://doi.org/10.1007/s00521-020-04951-2