JACIII Vol.19 No.3 pp. 343-348
doi: 10.20965/jaciii.2015.p0343


The Research of the Distributed Resource-Aware K-means Clustering Algorithm

Xiaoni Wang

School of Applied Science, Beijing Information Science and Technology University
No.12, Qing He Xiao Ying East Road, Haidian District, Beijing, China

October 19, 2014
January 27, 2015
May 20, 2015
distributed, resource-aware, clustering, adaptive, real-time
According to the characteristics of the constrained resource in distributed real-time data mining in the Internet of Things (IOT) environment, a distributed data mining method is researched in such environment. Based on the limits of computing ability, storage ability, battery energy resources, network bandwidth, and the Internet single point failure, the distributed network data mining method is researched, and the adaptive technology and peer-to-peer node method are adopted. The DRA-Kmeans algorithm of data mining based on the K-means algorithm is proposed, and the amount of data communication among the sites to reduce the number of iterations and clustering is reduced. Clustering efficiency is improved, and better clustering results and execution efficiency are achieved.
Cite this article as:
X. Wang, “The Research of the Distributed Resource-Aware K-means Clustering Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.3, pp. 343-348, 2015.
Data files:
  1. [1] J. Liang and Y. Tian, “Distributed K-means Clustering Algorithm,” Modern Electronics Technique, pp. 11-14, 2010.
  2. [2] X. Zhang and W. Zeng, “Research and advances of real-time data stream clustering,” Computer Engineering and Design, pp. 2177-2186, 2009.
  3. [3] Mohamed Medhat Gaber and Philip S. Yu, “A framework for resource-aware knowledge discovery in data streams: A holistic approach with its application,” Proc. of the ACM Symp. on Applied Computing, pp. 649-656, 2006.
  4. [4] C. C. Aggarwal, J. W. Han, J. Wang, and Philip S. Yu, “A framework for clustering evolving data streams,” Proc. of the 29th Int. Conf. on Very Large Data Bases, pp. 81-92, 2003.
  5. [5] J. D. Ren, W. W. Zhou, and H. T. He, “Adaptive Clustering Algorithm for Mining Subspace Clusters in High-Dimensional Data Stream,” J. of Frontiers of Computer Science and Technology, pp. 859-864, 2010.
  6. [6] S. Kantabutra and A. L. Couch, “Parallel k-means clustering algorithm on NOWs,” Technical J., pp. 243-247, 1999.
  7. [7] M. M. Zheng and G. L. Ji, “DK-Means-An Improved Distributed Clustering Algorithm,” J. of Computer Research and Development, pp. 84-88, 2007.
  8. [8] Y. Jiang, C. Yu, and N. Shen, “Diffused and emerging incremental clustering algorithm,” Computer Engineering and Design, pp. 2669-2672, 2012.
  9. [9] M. Rabbani, M. Baghersad, and R. Jafari, “A new hybrid GA-PSO method for solving multi-period inventory routing problem with considering financial decisions,” J. of Industrial Engineering and Management, pp. 909-929, 2013.
  10. [10] L. Georgescu, D. Zeitler, and C. R. Standridge, “Intelligent transportation system real time traffic speed prediction with minimal data,” J. of Industrial Engineering and Management, pp. 431-441, 2012.
  11. [11] X. Wang, “Resource-Aware Clustering Based AODVjr Routing Protocol in the Internet of Things,” J. of Advanced Computational Intelligenceand intelligent Informatics, Vol.17, No.4, pp. 622-627, 2013.
  12. [12] V. Fernandez, P. Simo, J. M. Sallan, and I. Trullas, “A preliminary panel data study about the progress of media richness,” J. of System and Management Sciences, pp. 16-21, 2013.
  13. [13] Y. Gao, Z. Zhang, H. Lu, and H. Wang, “Analysis and calculation of read distance in passive backscatter RFID systems,” J. of System and Management Sciences, pp. 34-41, 2013.

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