TRAN Dang Cong, WU Zhijian, WANG Zelin, DENG Changshou. A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K-Means[J]. Chinese Journal of Electronics, 2015, 24(4): 694-701. doi: 10.1049/cje.2015.10.006
Citation: TRAN Dang Cong, WU Zhijian, WANG Zelin, DENG Changshou. A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K-Means[J]. Chinese Journal of Electronics, 2015, 24(4): 694-701. doi: 10.1049/cje.2015.10.006

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

doi: 10.1049/cje.2015.10.006
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
  • 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.
  • loading
  • R. Xu and W. II Donald, "Survey of clustering algorithms", IEEE Trans. on Neural Networks, Vol.16, No.3, pp.645-678, 2005.
    J.A. Hartigan, Clustering Algorithms, 1st Edition, Wiley, New York, pp.351, 1975.
    Arthur and Vassilvitskii, "K-Means++: The advantages of careful seeding", Proc. of the eighteenth annual ACM-SIAM symposium on Discrete algorithms-SODA '07, pp.1027-1035, 2007.
    J.H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, 1975.
    Y. Shi and R.C. Eberhart, "A modified particle swarm optimizer", Proc. of IEEE Congress on Evolutionary Computation (CEC), pp.68-73, 1998.
    R. Storn and K. Price, "Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces", J. Global Optimiz., Vol.11, No.4, pp.341-359, 1997.
    D. Karaboga, "An idea based on honey bee swarm for numerical optimization", Technical report-TR06, Erciyes University, Engineering Faculty, Comput. Eng. Dep, 2005.
    D. Karaboga and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm", J. Global Optim, Vol.39, No.3, pp.459-471, 2007.
    W. Gao, S. Liu and L. Huang, "Inspired artificial bee colony algorithm for global optimization problems", Acta Electronica Sinica in Chinese, Vol.40, No.12, pp.2396-2403, 2012. (in Chinese)
    T. Liao, D. Aydn and T. Sttzle, "Artificial bee colonies for continuous optimization: Experimental analysis and improvements", Swarm Intell., Vol.7, No.4, pp.327-356, 2013.
    G. Zhu and S. Kwong, "Gbest-guided artificial bee colony algorithm for numerical function optimization", Applied Mathematics and Computation, Vol.217, No.7, pp.3166-3173, 2010.
    X. Yan, Y. Zhu, W. Zou, et al., "A new approach for data clustering using hybrid artificial bee colony algorithm", Neurocomputing, Vol.97, No.15, pp.241-250, 2012.
    W. Zou, Y. Zhu, H. Chen, et al., "A clustering approach using cooperative artificial bee colony algorithm", Discrete Dynamics in Nat. Soc., Vol.2010, Article ID 459796, pp.1-16, 2010.
    E. Mohammed, "Generalized opposition-based artificial bee colony algorithm", Proc. of IEEE Congress on Evolutionary Computation (CEC), pp.1-4, 2012.
    D.C. Tran, Z. Wu and H. Wang, "A new approach of diversity enhanced particle swarm optimization with neighbourhood search and adaptive mutation", Proc. of the 21st International Conference on Neural Information Processing (ICONIP2014), Vol.8835, pp.143-150, 2014.
    J. Derrac, "A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms", Swarm and Evol. Comput., Vol.1, No.1, pp.3-18, 2011.
    Y. Kao, E. Zahara and I. Kao, "A hybridized approach to data clustering", Expert Systems with Applications, Vol.34, No.3, pp.1754-1762, 2008.
    T. Niknam and B. Amiri, "An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis", Applied Soft Computing, Vol.10, No.1, pp.183-197, 2010.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (268) PDF downloads(1266) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return