Application of honey-bee mating optimization algorithm on clustering

Fathian, Mohammad; Amiri, Bahram; Maroosi, Ali

doi:10.1016/j.amc.2007.02.029

Cited by 220 publications

(97 citation statements)

References 11 publications

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“…The results of [17] show the performance of GSA-KM in comparison with K-means [1], GA [3], ACO [5], HBMO [6], PSO [7,11], SA [9], and the original GSA [10,11]. In this study, our experimental results indicate the superiority of the proposed GSA-KHM in comparison with GSA-KM and the other comparative methods.…”

Section: Resultssupporting

confidence: 54%

“…In this table, n is the size of the dataset, d is the number of features, and k is the number of clusters. The proposed GSA-KHM clustering approach is compared with the K-means [1], GA [3], ACO [5], HBMO [6], PSO [7,11], SA [9], original GSA [10,11], and GSA-KM [17] algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…Experiments have been conducted on five well-known UCI [18] datasets and, as a case study, it is applied on four text document datasets. The simulation results for both applications show the superiority of GSA-KHM over GSA-KM [17] and other previous methods including K-means [1], GA [3], ACO [5], HBMO [6], PSO [7,11], SA [9], and the original GSA [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…A combinational method of K-means and the genetic algorithm was proposed in [4]. Two clustering methods based on ant colony and honeybee mating were respectively proposed in [5] and [6]. Particle swarm optimization was used in clustering in [7] and [8].…”

Section: Introductionmentioning

confidence: 99%

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A clustering approach using a combination of gravitational search algorithm and k-harmonic means and its application in text document clustering

Mirhosseini¹

2017

Turk J Elec Eng & Comp Sci

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Data clustering is one of the most popular techniques of information management, which is used in many applications of science and engineering such as machine learning, pattern reorganization, image processing, data mining, and web mining. Different algorithms have been suggested by researchers, where the evolutionary algorithms are the best in data clustering and especially in big datasets. It is illustrated that GSA-KM, which is a combination of the gravitational search algorithm (GSA) and K-means (KM), is superior over some other comparative evolutionary methods. One of the drawbacks of this approach is dependency on the initial seeds. In this paper, a combination method of GSA and Kharmonic means, called GSA-KHM, has been proposed, in which the dependency on the initialization has been improved.The proposed GSA-KHM method has been applied to data clustering. As a special application, it has also been used on the text document clustering application. The simulation results show that the proposed method works better than the GSA-KM and other comparative methods in both data clustering and text document clustering applications.

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Section: Resultssupporting

confidence: 54%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A clustering approach using a combination of gravitational search algorithm and k-harmonic means and its application in text document clustering

Mirhosseini¹

2017

Turk J Elec Eng & Comp Sci

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“…The most popular class of clustering algorithms is K-means algorithm [3] which is a centre based, simple and fast algorithm but has the insufficiencies that it highly depends on the initial states and is easily trapped in local minima from the starting position of the search and global solutions of large problems cannot find with reasonable amount of computation effort [4]. In order to overcome local optima problem, the researchers from diverse fields are applying hierarchical clustering, partition-based clustering, density-based clustering, and artificial intelligence based clustering methods, such as: statistics [5], graph theory [6], expectation-maximization algorithms [7], artificial neural networks [8], evolutionary algorithms [9], swarm intelligence algorithms [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering

Danish¹,

Hassan²,

Badshah³

2018

IJCA

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Data clustering is a widespread data compression, vector quantization, data analysis and data mining technique. The principle objective of data clustering is to make clusters (or groups) such that data having high degree of similarity is gathered in the same cluster while data having high degree of dissimilarity is gathered in the different clusters and plays a key role for users to organize, summarize, and steer the data adequately. In this work Artificial Bee Colony (ABC) algorithm is applied to different size datasets. Results clearly show that ABC when applied on small size datasets were more effective than those of large size datasets in terms of intra-cluster distance, computation cycles and time required to complete those cycles.

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Bee Algorithms

2010

Engineering Optimization

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Application of honey-bee mating optimization algorithm on clustering

Cited by 220 publications

References 11 publications

A clustering approach using a combination of gravitational search algorithm and k-harmonic means and its application in text document clustering

A clustering approach using a combination of gravitational search algorithm and k-harmonic means and its application in text document clustering

Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering

Bee Algorithms

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