A new approach for data clustering using hybrid artificial bee colony algorithm

Yan, Xiaohui; Zhu, Yunlong; Zou, Wenping; Wang, Liang

doi:10.1016/j.neucom.2012.04.025

Cited by 145 publications

(67 citation statements)

References 34 publications

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“…The tests proved its performance in terms of better convergence, enhanced exploration and avoiding local optima traps. Y a n et al [181] developed a new version of ABC called Hybrid ABC algorithm for clustering by adding the crossover operator of GA to enhance the information exchange between bees and the searching ability of ABC. The results prove the better convergence speed and accuracy as compared to other algorithms.…”

Section: Abc Applications In Data Clusteringmentioning

confidence: 99%

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Kumar

Jarial

2017

Cybernetics and Information Technologies

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Section: Abc Applications In Data Clusteringmentioning

confidence: 99%

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Kumar

Jarial

2017

Cybernetics and Information Technologies

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“…The number of researches about the Swarm Intelligence methods, more specifically those based on the behavior of social bees, has increased significantly over the past years. [65][66][67][68] Inspired by the collective decision making process of social bees many methods have been proposed in the literature and the popularity of these methods has stimulated the development of several data mining approaches, such as clustering algorithms. [69] In this context, this paper proposes a multiobjective clustering algorithm, called cOptBees-MO, inspired by the foraging behavior of bee colonies.…”

Section: Coptbees-mo: a Multiobjective Bee-inspired Clustering Algorithmmentioning

confidence: 99%

Bio-inspired multiobjective clustering optimization: A survey and a proposal

Cunha

Cruz

Politi

et al. 2017

AIR

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Multiobjective clustering techniques have been used to simultaneously consider several complementary aspects of clustering quality. They optimize two or more cluster validity indices simultaneously, they lead to high-quality results, and have emerged as attractive and robust alternatives for solving clustering problems. This paper provides a brief review of bio-Inspired multiobjective clustering, and proposes a bee-inspired multiobjective optimization (MOO) algorithm, named cOptBees-MO, to solve multiobjective data clustering problems. In its survey part, a brief tutorial on MOO and multiobjective clustering optimization (MOCO) is presented, followed by a review of the main works in the area. Particular attention is given to the many objective functions used in MOCO. To evaluate the performance of the algorithm it was executed for various datasets and the results presented high quality clusters, diverse solutions an the automatic determination of a suitable number of clusters.

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“…Artificial Bee Colony (ABC) algorithm is a problem solving method, developed based on the behaviors of honey bee colony, searching and sharing the information with other colony members in the hive, to be able to find out richest food sources in shortest possible time [24,25,26,27].…”

Section: Artifcial Bee Colony (Abc) Algorithmmentioning

confidence: 99%

A Survey on Hesitation Information Mining

Pathak¹,

Tiwari²

2016

IJCA

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The tremendous advancement in technology has given rise to an increasing requirement for the storage of data in files, databases and other data repositories. As a result, decisionmakers are required to use new and powerful automated tools for the purpose of the analysis and interpretation of the stored data, as well as for the extraction of interesting patterns in data. Since the stored data is not always exact and precise, some means are required to handle this aspect of data and extract the useful information (e.g. hesitation information) arising from such uncertainties. For this purpose vague set theory has been applied for efficiently modeling the uncertainties that occur in datasets. Vague sets are an extension of the classical set theory. They extend the application of set theory to vague and uncertain problems. Vague set theory has emerged as a new tool to deal with the uncertainties of the data and the parameters attached to the data. This theory exhibits a very promising approach to analyzing uncertain data and deriving some interesting results suitable for use in various applications. This paper discusses the notions of vague sets and vague association rules. Also, optimization techniques have been introduced that would be helpful in optimizing the outcomes. When both are implemented together, a new approach will be created which is expected to generate much improved results. This paper emphasizes on the study of vague sets for a series of applications especially in decision making problems.

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A new approach for data clustering using hybrid artificial bee colony algorithm

Cited by 145 publications

References 34 publications

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Bio-inspired multiobjective clustering optimization: A survey and a proposal

A Survey on Hesitation Information Mining

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