Improved Artificial Bee Colony Clustering Algorithm Based on K-Means

Xue Mei Wang; Jin Bo Wang

doi:10.4028/www.scientific.net/AMM.556-562.3852

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Bacterial Foraging Optimization Algorithm with Quorum Sensing Mechanism
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 556-562Improved Artificial Bee Colony Clustering...

Improved Artificial Bee Colony Clustering Algorithm Based on K-Means

Abstract:

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. Artificial Bee Colony algorithm based on K-means was introduced in this article, then put forward an improved Artificial Bee Colony algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved algorithm stability of k-means clustering algorithm well, and more effectively improved clustering quality and property.

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Periodical:

Applied Mechanics and Materials (Volumes 556-562)

Pages:

3852-3855

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.556-562.3852

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Online since:

May 2014

Authors:

Xue Mei Wang*, Jin Bo Wang

Keywords:

Artificial Bee Colony (ABC) Algorithm, Cluster Analysis, K-Mean, Nonlinear Selection

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* - Corresponding Author

References

[1] FUDS, ZHOU. Improved K-means algorithm and its implementationbased on density. Journal of Computer Applications, 2011, 31( 2) : 432-434.

Google Scholar

[2] ZHANG W M, WU J, YUAN X J. K-means text clustering algorithmbased on desity and nearest neighbor. Journal of ComputerApplications, 2010, 30( 7) : 1933-(1935).

Google Scholar

[3] XIAO J, YAN Y P, ZHANG J, et al. A quantum-inspired geneticalgorithm for K-means clustering. Expert Systems with Applications, 2010, 37(7): 4966-4973.

DOI: 10.1016/j.eswa.2009.12.017

Google Scholar

[4] LUO J, WANG Q, FU L. Application of modified artificial bee colony algorithm to flatness error evaluation. Optics and PrecisionEngineering, 2012, 20( 2) : 422- 430.

DOI: 10.3788/ope.20122002.0422

Google Scholar

[5] WU Y, LI Y X, XU X. Novel oppositional hybrid differential evolutionalgorithm based on swarm intelligence. Journal of ChineseComputer Systems, 2009, 30( 5) : 903-907.

Google Scholar

[6] AGUSTIN-BLAS L E, SALCEDO-SANZ S, JIMENEZ-FEＲNANDEZS, et al. A new grouping genetic algorithm for clusteringproblems. Expert Systems with Applications, 2012, 39 (10): 9695-9703.

DOI: 10.1016/j.eswa.2012.02.149

Google Scholar