Comparative Analysis of Clustering Techniques in Cloud For Effective Load Balancing

  • Authors

    • Akankshya Aparajita
    • Shrabanee Swagatika
    • Debabrata Singh
    https://doi.org/10.14419/ijet.v7i3.4.14674

    Received date: June 26, 2018

    Accepted date: June 26, 2018

    Published date: June 25, 2018

  • Clustering, Data mining, Partition-based, Hierarchical-based, Density-based, Grid-based, Cloud computing.

  • Abstract

    Clustering is used as an important procedure in the process of data mining, where information of large datasets are transformed into meaningful and concise data. It performs activities like pattern representation, using of clustering algorithms and their validation, data abstraction and finally result generated. Clustering has many categories of algorithms such as partition-based, hierarchical-based, density-based, grid-based etc. Partition-based is the centroid-based clustering. Hierarchical-based clustering is link-based. Density-based is clustering is focused on area of higher density in the dataset. Grid-based clustering relies on size of the grid. In this paper, we discussed different clustering techniques as well as, a detailed review on the partition-based and hierarchical-based algorithms. Finally we compare clustering algorithms on the basis of attributes like time complexity, capacity of handling large datasets, scalability, sensitivity to outliers and noise, and also discussed result after solving a particular dataset implemented in cloud computing environment.

  • References

    1. Rui Xu and Donald Wunsch., “Survey of clustering algorithms”, IEEE Transactions on neural networks, Vol-16, Issue- 3, pp. 645-678, 2005.
    2. Banerjee, A., Pati, B. and Panigrahi, C.R, “SC2: A Selection-Based Consensus Clustering Approach”, Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2016, 2, p.177, 2017.
    3. Yang, Haibo, and Mary Tate, “A descriptive literature review and classification of cloud computing research”, CAIS 31, 2, 2012.
    4. Ambroise, Christophe, Geniève Sèze, Fouad Badran, and Sylvie Thiria, “Hierarchical clustering of self-organizing maps for cloud classification”, Neurocomputing 30, no. 1-4, pp. 47-52, 2000.
    5. Weinhardt, Christof, Arun Anandasivam, Benjamin Blau, Nikolay Borissov, Thomas Meinl, Wibke Michalk, and Jochen Stößer, “Cloud computing–a classification, business models, and research directions”, Business & Information Systems Engineering, Vol-1, no. 5, pp.391-399, 2009.
    6. Swagatika Shrabanee, Rath Amiya Kumar, Pattnaik Prasant Kumar, Markov Chain Model And PSO Technique for Dynamic Heuristic Resource Scheduling for System Level Optimization of Cloud Resources, ARPN Journal of Engineering and Applied Sciences. 13(7): 375-393, 2018.
    7. Fritzke, Bernd. "Growing cell structures—a self-organizing network for unsupervised and supervised learning”, Neural networks, Vol-7, no. 9, pp.1441-1460, 1994.
    8. Sindhwani, Vikas, Partha Niyogi, and Mikhail Belkin, “Beyond the point cloud: from transductive to semi-supervised learning”, In Proceedings of the 22nd international conference on Machine learning, pp. 824-831. ACM, 2005.
    9. Andrea Baraldi and Ethem Alpaydin, “Constructive feed forward ART clustering networks”, IEEE transactions on neural networks 13, no. 3, pp. 645-661, 2002.
    10. Panda, Prasanta Kumar, and Swagatika Shrabanee. "Energy consumption in cloud computing and power management”, International Journal of Advanced Research in Computer Science, Vol- 3, no. 2, 2012.
    11. Bernd Fritzke, “Some competitive learning methods”, Artificial Intelligence Institute, Dresden University of Technology , 1997.
    12. Zhang, Qingchen, Laurence T. Yang, Zhikui Chen, and Peng Li, “PPHOPCM: privacy-preserving high-order possibilistic c-means algorithm for big data clustering with cloud computing”, IEEE Transactions on Big Data, 2017.
    13. Chris Fraley, and Adrian E. Raftery, “MCLUST: Software for model-based cluster analysis”, Journal of classification 16, no. 2, pp.297-306, 1999.
    14. Anil K. Jain, Robert P. W. Duin, and Jianchang Mao, “Statistical pattern recognition: A review”, IEEE Transactions on pattern analysis and machine intelligence, Vol-22, no. 1, pp.4-37, 2000.
    15. Gu, Guofei, Roberto Perdisci, Junjie Zhang, and Wenke Lee, “BotMiner: Clustering Analysis of Network Traffic for Protocol-and Structure-Independent Botnet Detection”, In USENIX security symposium, vol. 5, no. 2, pp. 139-154. 2008.
    16. Ryszard S. Michalski, and Robert E. Stepp, “Automated construction of classifications: Conceptual clustering versus numerical taxonomy”,IEEE Transactions on pattern analysis and machine intelligence,pp.396-410,1983.
    17. Victor L. Brailovsky, “A probabilistic approach to clustering”, Pattern Recognition Letters 12, no. 4, pp.193-198, 1991.
    18. D.Singh,B. Pattanayak,C. Panda, “Analysis of an Improved Energy Balanced Routing Protocol for Wireless Sensor Network”, IEEE International conference on Communication and Signal processing(ICCSP- 2016),pp-1503-1507, 2016.
    19. John H. Holland, “Adaptation in natural and artificial systems. An introductory analysis with application to biology, control, and artificial intelligence”, Ann Arbor, MI: University of Michigan Press, pp. 439-444, 1975.
    20. Jianchang Mao, and Anil K. Jain, “Texture classification and segmentation using multi resolution simultaneous autoregressive models”, Pattern recognition 25, no. 2, pp.173-188, 1992.
    21. Mahendiran, A., N. Saravanan, N. Venkata Subramanian, and N. Sairam, “Implementation of K-means clustering in cloud computing environment”, Research Journal of Applied Sciences, Engineering and Technology, Vol- 4, no. 10, pp.1391-1394, 2012.
    22. Karen L. Oehler, and Robert M. Gray, “Combining image compression and classification using vector quantization”, IEEE transactions on pattern analysis and machine intelligence 17, no. 5, pp. 461-473,1995.
    23. Berkhin, Pavel, “A survey of clustering data mining technique”, In grouping multidimensional data, pp. 25-71. Springer, Berlin, Heidelberg, 2006.
    24. Fionn Murtagh, “A survey of recent advances in hierarchical clustering algorithms”, The Computer Journal 26, no. 4, pp. 354-359, 1983.
    25. Andrea Baraldi, and Palma Blonda, “A survey of fuzzy clustering algorithms for pattern recognition”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 29, no. 6, pp.778-785, 1999.
    26. Swagatika, Shrabanee, and Pattnaik P. K., “Design Criteria of SOA for Cloud Based Infrastructure Resource Management as a Service”, International Journal of Instrumentation, Control & Automation (IJICA),Vol- 1, no-1 ,2011.
    27. A.Rauber, J. Paralic, and E. Pampalk, “Empirical evaluation of clustering algorithms,” J. Inf. Org. Sci., vol. 24, no. 2, pp. 195-209, 2000.
    28. Chih-Ping Wei, Yen-Hsien Lee, and Che-Ming Hsu, “Empirical comparison of fast clustering algorithms for large data sets”, In System Sciences, 2000. Proceedings of the 33rd Annual Hawaii International Conference on, pp. 10-pp. IEEE, 2000.
    29. Scheunders, Paul, “A comparison of clustering algorithms applied to color image quantization”, Pattern Recognition Letters, Vol-18, no. 11-13, pp.1379-1384, 1997.
    30. Boomija, M. D., and M. Phil, “Comparison of partition based clustering algorithms”, Journal of Computer Applications, Vol-1, no. 4, pp.18-21, 2008.
    31. Ahmad, Amir, and Lipika Dey, “A k-mean clustering algorithm for mixed numeric and categorical data”, Data & Knowledge Engineering, Vol-63, no. 2, pp.503-527, 2007.
    32. D. Singh, B.K. Pattanayak, “Analytical Study of an Improved Cluster based Routing Protocol in Wireless Sensor Network”, Indian Journal of Science and Technology, Vol 9(37), DOI: 10.17485/ijst/2016/v9i37/97947, October 2016.

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  • How to Cite

    Aparajita, A., Swagatika, S., & Singh, D. (2018). Comparative Analysis of Clustering Techniques in Cloud For Effective Load Balancing. International Journal of Engineering and Technology, 7(3.4), 47-51. https://doi.org/10.14419/ijet.v7i3.4.14674