A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and <em>K</em>-Means

TRAN Dang Cong; WU Zhijian; WANG Zelin; DENG Changshou

doi:10.1049/cje.2015.10.006

Volume 24 Issue 4

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Article Navigation > Chinese Journal of Electronics > 2015 > 24(4): 694-701

TRAN Dang Cong, WU Zhijian, WANG Zelin, et al., “A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K-Means,” Chinese Journal of Electronics, vol. 24, no. 4, pp. 694-701, 2015, doi: 10.1049/cje.2015.10.006

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TRAN Dang Cong, WU Zhijian, WANG Zelin, et al., “A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K-Means,” Chinese Journal of Electronics, vol. 24, no. 4, pp. 694-701, 2015, doi: 10.1049/cje.2015.10.006

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A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K-Means

doi: 10.1049/cje.2015.10.006

1.
State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan 430072, China;
2.
Vietnam Academy of Science and Technology, Hanoi, Vietnam;
3.
School of Information Science and Technology, Jiujiang University, Jiujiang 332005, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61070008, No.61364025), and the Science and Technology Program of Nantong, China (No.BK2014057).

More Information

Corresponding author: WU Zhijian (corresponding author)is a full professor and worksfor the State Key Laboratory of SoftwareEngineering, School of Computer,Wuhan University, Wuhan, China. Hiscurrent research interests include evolutionarycomputation, intelligent computing,parallel computing. (Email: zhijianwu@whu.edu.cn)
Received Date: 2014-02-21
Rev Recd Date: 2015-01-23
Publish Date: 2015-10-10

Abstract

Abstract

To improve the performance of K-means clustering algorithm, this paper presents a new hybrid approach of Enhanced artificial bee colony algorithm and K-means (EABCK). In EABCK, the original artificial bee colony algorithm (called ABC) is enhanced by a new mutation operation and guided by the global best solution (called EABC). Then, the best solution is updated by K-means in each iteration for data clustering. In the experiments, a set of benchmark functions was used to evaluate the performance of EABC with other comparative ABC variants. To evaluate the performance of EABCK on data clustering, eleven benchmark datasets were utilized. The experimental results show that EABC and EABCK outperform other comparative ABC variants and data clustering algorithms, respectively.
- Data clustering,
- K-means,
- Artificial bee colony,
- Mutation operation

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References(18)

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