Skip to main content
Log in

Optimization using Artificial Bee Colony based clustering approach for big data

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

As one of the major problems is that the time taken for executing the traditional algorithm is larger and that it is very difficult for processing large amount of data. Clusters possess high degree of similarity among each cluster and have low degree of similarity among other clusters. Optimization algorithm for clustering is the art of allocating scarce resources to the best possible effect. The traditional optimization algorithm is not suitable for processing high dimensional data. The main objective of proposed Artificial Bee Colony (ABC) approach is to minimize the execution time and to optimize the best cluster for the various sizes of the dataset. To deal with this, we are normalizing to distributed environment for time efficiency and accuracy. The proposed ABC algorithm simulates the behavior of real bees for solving numerical optimization problems particularly in clustering. The dataset size is varied for the algorithm and is mapped with its appropriate timings. The result is observed for various fitness and probability value which is obtained from the employed and the onlooker phase of ABC algorithm from which the further calibrations of classification error percentage is done. The proposed ABC Algorithm is implemented in Hadoop environment using mapper and reducer programming. An experimental result reveals that the proposed ABC scheme reduces the execution time and classification error for selecting optimal clusters. The results show that the proposed ABC scheme gives a better performance than PSO and DE in terms of time efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Katal, A., Wazid, M., Goudar, R.H.: Big data: issues, challenges, tools and Good practices. NCCI 10, 404–409 (2013)

    Google Scholar 

  2. Karaboga, D., Ozturk, C.: A novel clustering approach: Artificial Bee Colony(ABC) algorithm. Appl. Soft Comput. 11, 652–657 (2011)

    Article  Google Scholar 

  3. De Falco, I., Della Cioppa, A., Tarantino, E.: Facing classification problems with Particle Swarm Optimization. Appl. Soft Comput. 7, 652–658 (2007)

    Article  Google Scholar 

  4. Sarkar, M., Yegnanafayana, B., Khemani, D.: A clustering algorithm using an evolutionary programming based approach. Pattern Recognit. 18, 975–986 (2007)

    Article  Google Scholar 

  5. Siddiqa, Aisha: Hashem, IbrahimAbakerTargio: A survey of big data management: taxonomy and state-of-the-art. J. Netw. Comput. Appl. 71, 151–166 (2006)

    Article  Google Scholar 

  6. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8, 687–697 (2008)

    Article  Google Scholar 

  7. Nadeem, A., Mohad Vasim, A., Shahbaz, K.: Clustering on Big Data using Hadoop MapReduce. In: 2012 International Conference on Computational Intelligence and Communication Networks, vol. 7, pp. 652–658 (2012)

  8. Suresh, A., Shunmuganathan, K.L.: Image texture classification using gray level co-occurrence matrix based statistical features. Eur. J. Sci. Res. 75(4), 591–597 (2012)

    Google Scholar 

  9. Laney, D.: 3D Data management: controlling data volume, velocity and variety. Appl. Deliv. Strateg. Meta Group 7, 949–956 (2011)

    Google Scholar 

  10. Zhang, C., Ouyang, D., Ning, J.: An artificial bee colony approach for clustering. Expert Syst. Appl. 37, 4761–4767 (2010)

    Article  Google Scholar 

  11. Karaboga, D., Akay, B.: An idea based on honey bee swarm for numerical optimization. ICCC 32, 452–463 (2015)

    Google Scholar 

  12. Tasgetiren, M.F., Pan, Q.K., Suganthan, P.N., Chen, A.H.L.: A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops. Inf. Sci. 181, 3459–3475 (2011)

    Article  MathSciNet  Google Scholar 

  13. Vimal, S., Kalaivani, L., Kaliappan, M.: Collaborative approach on mitigating spectrum sensing data hijack attack and dynamic spectrum allocation based on CASG modeling in wireless cognitive radio networks. Clust. Comput. (2017). https://doi.org/10.1007/s10586-017-1092-0

  14. Subbulakshmi, P., Prakash, M., Ramalakshmi, V.: Honest auction based spectrum assignment and exploiting spectrum sensing data falsification attack using stochastic game theory in wireless cognitive radio network. Wireless. Pers. Commun. (2017). https://doi.org/10.1007/s11277-017-5105-3

  15. Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization method for constrained optimization problems. Appl. Comput. 22, 525–534 (2012)

    Google Scholar 

  16. Zou, W., Zhu, Y., Chen, H., Sui, X.: A clustering approach using Cooperative artificial bee colony. Discret. Dyn. Nat. Soc. 201, 16–24 (2010)

    MathSciNet  MATH  Google Scholar 

  17. de Oliveira, I.M.S., Schirru, R., de Medeirose, J.A.C.C.: On the performance of an Artificial Bee Colony Algorithm applied to the accident diagnosis in nuclear power plant. In: International Nuclear Atlantic Conference (INAC), Rio de Janeiro, vol. 3, pp. 978–985 (2009)

  18. Pansuwan, P., Rukwong, N., Pongcharoen, P.: Identifying optimum Artificial Bee Colony (ABC) algorithm’s parameters for scheduling the manufacture and assembly of complex products. ICCNT 12, 339–343 (2010)

    Google Scholar 

  19. Suresh, A.: An efficient view classification of echocardiogram using morphological operations. J. Theor. Appl. Inf. Technol. (JATIT) 67(3), 732–735 (2014)

    MathSciNet  Google Scholar 

  20. Shim, K.: MapReduce algorithms for big data analysis. In: Madaan, A., Kikuchi, S., Bhalla, S. (eds.) Databases in Networked Information Systems (DNIS 2013). Lecture Notes in Computer Science, vol. 7813. Springer, Berlin (2013)

  21. Suresh, A., Varatharajan, R.: Competent resource provisioning and distribution techniques for cloud computing environment. Clust. Comput. (Online). https://doi.org/10.1007/s10586-017-1293-6

  22. Blake, C.L., Merz,, C.J.: University of California at Irvine Repository of Machine Learning Databases. http://www.ics.uci.edu/~mlearn/MLRepository.html (1998)

  23. Subbulakshmi, P., Vimal, S.: Secure data packet transmission in Manet using enhanced identity-based cryptography (EIBC). Int. J. New Technol. Sci. Eng. 3(12), 35–42 (2016)

    Google Scholar 

  24. Kaliappan, M., Paramasivam, B.: Enhancing secure routing in mobile ad hoc networks using a dynamic Bayesian signalling game model. Comput. Electr. Eng. 41, 301–313 (2015)

    Article  Google Scholar 

  25. Mariappan, E., Kaliappan, M., Vimal, S.: Energy efficient routing protocol using Grover’s searching algorithm for MANET. Asian J. Inf. Technol. 15, 4986–4994. https://doi.org/10.3923/ajit.2016.4986.4994 (2016)

  26. Koundinya, A.K., Srinath, N.K.: MapReduce design of K-means clustering algorithm. In: 2013 International Conference on Information Science and Applications (ICISA), vol. 53, pp. 652–663 (2013)

  27. Karaboga, D.: Artificial Bee Colony Algorithm. Scholarpedia 5(3), 6915–6925 (2010)

    Google Scholar 

  28. Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization method for constrained optimization problems. In: Sincak, P., Vascak, J., et al. (eds.) Intelligent Technologies—Theory and Application: New Trends in Intelligent Technologies. Frontiers in Artificial Intelligence and Applications, vol. 76, pp. 214–220. IOS Press, Amsterdam (2008)

  29. Vigneswari, T., Maluk Mohamed, M.A.: Scheduling in sensor grid middleware for telemedicine using ABC algorithm. Int. J. Telemed. Appl. 10, 584–591 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Sudhakar Ilango.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ilango, S.S., Vimal, S., Kaliappan, M. et al. Optimization using Artificial Bee Colony based clustering approach for big data. Cluster Comput 22 (Suppl 5), 12169–12177 (2019). https://doi.org/10.1007/s10586-017-1571-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1571-3

Keywords

Navigation