Abstract
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.
Keywords
- Data Stream
- Dense Cluster
- Candidate Point
- Partition Method
- Compact Point
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your institution.
Buying options
Preview
Unable to display preview. Download preview PDF.
References
Henzinger, M., Raghavan, P., Rajagopalan, S.: Computing on Data Streams. Digital Eauipment Corporation, TR-1998-011 (August 1998)
Munro, J., Paterson, M.: Selection and Sorting with Limited Storage. Theoretical Computer Science, 315–323 (1980)
Flajolet, P., Martin, G.: Probabilistic counting algorithms for data base applications. JCSS 31, 182–209 (1985)
Alon, N., Matias, Y., Szegedy, M.: The space complexity of approximating the frequency moments. In: Proc. STOC, pp. 20–29 (1996)
Mirchandani, P., Francis, R. (eds.): Discrete Location Theory. John Wiley and Sons, Inc., New York (1990)
Managasarian, O.L.: Mathematical programming in data mining. Data Mining and Knowledge Discovery (1997)
Shmoys, D.B., Tardos, E., Aardal, K.: Approximation algorithms for facility location problems. In: Proc. STOC, pp. 265–274 (1997)
Charikar, M., Guha, S., Tardos, E., Shmoys, D.B.: A constant factor approximation algorithm for the k-median problem. In: Proc. STOC (1999)
Jain, K., Vazirani, V.: Primal-dual Approximation algorithms for metric facility location and k-median problems. In: Proc. FOCS (1999)
Charikar, M., Chekuri, C., Feder, T., Motwani, R.: Incremental clustering and dynamic information retrieval. In: In: Proc. STOC, pp. 626–635 (1997)
Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2000)
Guha, S., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering data stream. In: Proc FOCS, pp. 359–366 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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. https://doi.org/10.1007/978-3-540-24655-8_44
Download citation
DOI: https://doi.org/10.1007/978-3-540-24655-8_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21371-0
Online ISBN: 978-3-540-24655-8
eBook Packages: Springer Book Archive