CL2: A Multi-dimensional Clustering Approach in Sensor Networks

  • Xiuli Ma
  • Shuangfeng Li
  • Dongqing Yang
  • Shiwei Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3289)


Sensor networks are among the fastest growing technologies that have the potential of changing our lives drastically. These collaborative, dynamic and distributed computing and communicating systems are generating large amounts of data continuously. Finding useful patterns in large sensor data sets is a tempting however challenging task. In this paper, a clustering approach, CL2, CLuster and CLique, is proposed. CL2 can not only identify clusters in a multi-dimensional sensor dataset, discover the overall distribution patterns of the dataset, but also can be used for partitioning the sensor nodes into subgroups for task subdivision or energy management. CL2’s time efficiency, and accuracy of mining are evaluated through several experiments. A theoretic analysis of the algorithm is also presented.


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  1. 1.
    Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A Framework for Clustering Evolving Data Streams. In: VLDB 2003 (2003)Google Scholar
  2. 2.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Computer Networks 38(4), 393–422 (2002)CrossRefGoogle Scholar
  3. 3.
    Bandyopadhyay, S., Coyle, E.J.: An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks. In: IEEE INFOCOM 2003 (2003)Google Scholar
  4. 4.
    Ester, M., Kriegel, H., Sander, J., Wimmer, M., Xu, X.: Incremental clustering for mining in a data warehousing environment. In: VLDB 1998 (1998)Google Scholar
  5. 5.
    Franklin, M.J.: Challenges in Ubiquitous Data Management. In: Informatics 2001(2001)Google Scholar
  6. 6.
    Ghiasi, S., Srivastava, A., Yang, X., Sarrafzadeh, M.: Optimal Energy Aware Clustering in Sensor Networks. Sensors 2, 258–269 (2002)CrossRefGoogle Scholar
  7. 7.
    Han, J., Kamber, M.: Data Mining – Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  8. 8.
    Madden, S., Franklin, M.J.: Fjording the stream: An architecture for queries over streaming sensor data. In: ICDE 2002 (2002)Google Scholar
  9. 9.
    Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: The Design of an Acquisitional Query Processor For Sensor Networks. In: SIGMOD 2003 (2003)Google Scholar
  10. 10.
    Park, N.H., Lee, W.S.: Statistical Grid-based Clustering over Data Streams. SIGMOD Record 33(1) (March 2004)Google Scholar
  11. 11.
    Perich, F., Joshi, A., Finin, T., Yesha, Y.: On data management in pervasive computing environments. IEEE Transactions on Knowledge and Data Engineering 16(5) ( May 2004)Google Scholar
  12. 12.
    Pottie, G.J., Kaiser, W.J.: Wireless Integrated Network Sensors. Communications of the ACM 43(5), 51–58 (2000)CrossRefGoogle Scholar
  13. 13.
    Yao, Y., Gehrke, J.: Query processing for sensor networks. In: CIDR 2003 (2003)Google Scholar
  14. 14.
    Younis, O., Fahmy, S.: Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy- efficient Approach. In: IEEE INFOCOM 2004 (2004)Google Scholar
  15. 15.
    Zaki, M.J.: Scalable Algorithms for Association Mining. In: IEEE Transactions on Knowledge and Data Engineering, vol.12(3) (May/June 2000)Google Scholar
  16. 16.
    Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: An efficient data clustering method for very large databases. In: SIGMOD 1996 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Xiuli Ma
    • 1
    • 2
  • Shuangfeng Li
    • 1
  • Dongqing Yang
    • 1
  • Shiwei Tang
    • 1
    • 2
  1. 1.School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina
  2. 2.National Laboratory on Machine PerceptionPeking UniversityBeijingChina

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