Abstract
Due to in the early period of the ant colony clustering algorithm convergence speed is very slow, this paper proposes a hybrid clustering algorithm based on ant colony clustering and MMK-means algorithm, which uses MMK-means algorithm to process the data, followed by ant colony clustering to finish clustering. Apart from that, this paper improves the ant colony clustering algorithm that makes ants using the best matching position, data object placement selecting and so on. We realize the algorithm in Hadoop platform, which can effectively reduce the time costs of clustering.
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This work was supported by Open Subject Funds of Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory (ITD-U15002/KX152600011). NSFC(61401033,61372108,61272515). National Science and Technology Pillar Program Project (2015BAI11B01).
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References
Wei, X.: Clustering algorithm based on the combination of genetic algorithm and ant colony algorithm. In: International Conference on Innovative Computing & Cloud Computing, pp. 45–49. ACM (2011)
Kenidra, B., Meshoul, S.: A data-clustering approach based on artificial ant colonies with control of emergence. In: Soft Computing and Pattern Recognition, pp. 430–435. IEEE (2014)
Asbern, A., Asha, P.: Performance evaluation of association mining in Hadoop single node cluster with big data. In: International Conference on Circuit, Power and Computing Technologies. IEEE (2015)
Jiang, H., Zhang, G., Cai, J.: An improved ant colony clustering algorithm based on lf algorithm. In: 2015 IEEE 12th International Conference on e-Business Engineering (ICEBE), pp. 194–197. IEEE Computer Society (2015)
Yu, H., Wang, D.: Mass log data processing and mining based on Hadoop and cloud computing. In: International Conference on Computer Science & Education, pp. 197–202 (2012)
Zhou, A., Wang, S., Sun, Q., et al.: Dynamic virtual resource renting method for maximizing the profits of a cloud service provider in a dynamic pricing model. In: International Conference on Parallel and Distributed Systems, pp. 944–945 (2013)
Wang, S., Zhou, A., Hsu, C.H., et al.: Provision of data-intensive services through energy- and QoS-aware virtual machine placement in national cloud data centers. IEEE Trans. Emerg. Top. Comput. 4(2), 1 (2015)
Mao, L., Shen, M.M.: An improved ant colony clustering algorithm based on dynamic neighborhood. In: IEEE International Conference on Intelligent Computing and Intelligent Systems, pp. 730–734 (2010)
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, Z., Huo, Y., Wang, J., Zhao, K., Yang, Y. (2017). Research on Ant Colony Clustering Algorithm Based on HADOOP Platform. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_49
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DOI: https://doi.org/10.1007/978-3-319-59288-6_49
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