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Clustering Data Streams On the Two-Tier Structure

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 3007)


We put forward the framework of 2 levels structure to cluster the data streams. The first is Quickly Computing Level that gains the intermediate results with the rough but fast algorithm; the second is Complex Analysis Level that deeply analyzes the intermediate results with more complicated method to find complex clusters. The empirical evidence shows that the framework is satisfied with the demand of better quality based on effectively clustering the data streams.


  • Data Stream
  • Dense Cluster
  • Candidate Point
  • Partition Method
  • Compact Point

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  • DOI: 10.1007/978-3-540-24655-8_44
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  1. Henzinger, M., Raghavan, P., Rajagopalan, S.: Computing on Data Streams. Digital Eauipment Corporation, TR-1998-011 (August 1998)

    Google Scholar 

  2. Munro, J., Paterson, M.: Selection and Sorting with Limited Storage. Theoretical Computer Science, 315–323 (1980)

    Google Scholar 

  3. Flajolet, P., Martin, G.: Probabilistic counting algorithms for data base applications. JCSS 31, 182–209 (1985)

    MATH  MathSciNet  Google Scholar 

  4. Alon, N., Matias, Y., Szegedy, M.: The space complexity of approximating the frequency moments. In: Proc. STOC, pp. 20–29 (1996)

    Google Scholar 

  5. Mirchandani, P., Francis, R. (eds.): Discrete Location Theory. John Wiley and Sons, Inc., New York (1990)

    MATH  Google Scholar 

  6. Managasarian, O.L.: Mathematical programming in data mining. Data Mining and Knowledge Discovery (1997)

    Google Scholar 

  7. Shmoys, D.B., Tardos, E., Aardal, K.: Approximation algorithms for facility location problems. In: Proc. STOC, pp. 265–274 (1997)

    Google Scholar 

  8. Charikar, M., Guha, S., Tardos, E., Shmoys, D.B.: A constant factor approximation algorithm for the k-median problem. In: Proc. STOC (1999)

    Google Scholar 

  9. Jain, K., Vazirani, V.: Primal-dual Approximation algorithms for metric facility location and k-median problems. In: Proc. FOCS (1999)

    Google Scholar 

  10. Charikar, M., Chekuri, C., Feder, T., Motwani, R.: Incremental clustering and dynamic information retrieval. In: In: Proc. STOC, pp. 626–635 (1997)

    Google Scholar 

  11. Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2000)

    Google Scholar 

  12. Guha, S., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering data stream. In: Proc FOCS, pp. 359–366 (2000)

    Google Scholar 

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© 2004 Springer-Verlag Berlin Heidelberg

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Wang, Z., Wang, B., Zhou, C., Xu, X. (2004). Clustering Data Streams On the Two-Tier Structure. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds) Advanced Web Technologies and Applications. APWeb 2004. Lecture Notes in Computer Science, vol 3007. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21371-0

  • Online ISBN: 978-3-540-24655-8

  • eBook Packages: Springer Book Archive