article

An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering

Authors:

Elahe Taherian Fard,

Narges Pourjafarian,

Alireza RoustaAuthors Info & Claims

Engineering Applications of Artificial Intelligence, Volume 24, Issue 2

Pages 306 - 317

https://doi.org/10.1016/j.engappai.2010.10.001

Published: 01 March 2011 Publication History

Abstract

Clustering techniques have received attention in many fields of study such as engineering, medicine, biology and data mining. The aim of clustering is to collect data points. The K-means algorithm is one of the most common techniques used for clustering. However, the results of K-means depend on the initial state and converge to local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Modify Imperialist Competitive Algorithm (MICA) and K-means (K), which is called K-MICA, for optimum clustering N objects into K clusters. The new Hybrid K-ICA algorithm is tested on several data sets and its performance is compared with those of MICA, ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and K-means. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handling data clustering.

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Tang YZhou F(2023)An improved imperialist competition algorithm with adaptive differential mutation assimilation strategy for function optimizationExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118686211:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118686
Ay MÖzbakır LKulluk SGülmez BÖztürk GÖzer S(2023)FC-KmeansExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118656211:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118656
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Copyright © Elsevier Ltd © 2010.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 March 2011

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Cited By

Biswas TGiri KRoy S(2023)ECKMExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118862212:COnline publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118862
Tang YZhou F(2023)An improved imperialist competition algorithm with adaptive differential mutation assimilation strategy for function optimizationExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118686211:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118686
Ay MÖzbakır LKulluk SGülmez BÖztürk GÖzer S(2023)FC-KmeansExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118656211:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118656
Golzari Oskouei AHashemzadeh MAsheghi BBalafar M(2022)CGFFCMApplied Soft Computing10.1016/j.asoc.2021.108005113:PBOnline publication date: 3-Jan-2022
https://dl.acm.org/doi/10.1016/j.asoc.2021.108005
Hashemi STavana MBakhshi M(2022)A New Particle Swarm Optimization Algorithm for Optimizing Big Data ClusteringSN Computer Science10.1007/s42979-022-01208-83:4Online publication date: 30-May-2022
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Garain ADas D(2020)K-RMS AlgorithmProcedia Computer Science10.1016/j.procs.2020.03.188167:C(113-120)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1016/j.procs.2020.03.188
Pantula PMiriyala SMitra K(2020)An Evolutionary Neuro-Fuzzy C-means Clustering TechniqueEngineering Applications of Artificial Intelligence10.1016/j.engappai.2019.10343589:COnline publication date: 1-Mar-2020
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Shahidinejad AGhobaei-Arani MMasdari M(2020)Resource provisioning using workload clustering in cloud computing environment: a hybrid approachCluster Computing10.1007/s10586-020-03107-024:1(319-342)Online publication date: 23-Apr-2020
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Dinkar SDeep K(2020)Opposition-based antlion optimizer using Cauchy distribution and its application to data clustering problemNeural Computing and Applications10.1007/s00521-019-04174-032:11(6967-6995)Online publication date: 1-Jun-2020
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