A parameterized framework for clustering streams

International Journal of Data Warehousing and Mining

Volumn 5, Issue 1, 2009, Pages 36-56

  Bhatnagar, Vasudha ^a   Kaur, Sharanjit ^a   Mignet, Laurent ^b  

^a UNIVERSITY OF DELHI   (India)

^b IBM RESEARCH INDIA   (India)

Author keywords

Architecture; Clustering; Data stream; Grid; Micro cluster

Indexed keywords

ARCHITECTURE; CLUSTER ANALYSIS; CLUSTER COMPUTING; DATA COMMUNICATION SYSTEMS; NETWORK FUNCTION VIRTUALIZATION; PARAMETERIZATION;

APPLICATION REQUIREMENTS; ARCHITECTURAL FRAMEWORKS; CLUSTERING; COMPARATIVE STUDIES; DATA CHARACTERISTICS; DATA STREAM; GRID; MICRO-CLUSTERS;

CLUSTERING ALGORITHMS;

EID: 70249114396     PISSN: 15483924     EISSN: 15483932     Source Type: Journal    
DOI: 10.4018/jdwm.2009010103     Document Type: Article

References (34)

1. Aggarwal, C.C. (2007). 'Data Streams: Models and Algorithms', Springer Science+ Business Media, New York.
2. Aggarwal, C. C., Han, J., Wang, J., & Yu, P. S. (2003). 'A Framework for Clustering Evolving Data Streams', In Proceedings of the 29th VLDB conference, pp.81-92.
3. Aggarwal, C. C., Han, J., & Yu, P. S. (2004). 'A Framework for Projected Clustering of High Dimensional Data Streams', In Proceedings of International Conference on Very Large Data Bases, Toronto, Canada, pp.852-863.
4. Agrawal, R., Gehrke, J., Dimitrious, G., & Raghavan, P. (1998). 'Automatic Subspace Clustering of High Dimensional data for Data Mining application', ACM SIGMOD Record, pp.94-105.
5. Babcock, B., Babu, S., Datar, M., Motwani, R., & Widom, J. (2002). 'Models and Issues in Data Stream Systems', In Proceedings of the ACM Symposium on Principles of Database Systems (PODS), pp.1-16.
6. Barb́ara, D. (2002). 'Requirements of Clustering Data Streams', ACM SIGKDD Explorations Newsletter, Vol. 3, pp.23-27.
7. Berkhin, P. (2003). 'Survey of Clustering Data Mining Techniques', Accure Software Inc
8. Bhatnagar, V., & Kaur, S. (2007). 'Exclusive and Complete Clustering of Streams', In Proceedings of the International Conference on Database and Expert Systems Applications, Germany, pp.629-638.
9. Cao, F., Ester, M., Qian, W., & Zhou, A. (2006) 'Density-Based Clustering over an Evolving Data Stream with Noise', In Proceedings of the SIAM Conference on Data Mining, pp.326-337.
10. Carney, D., Cetintemel, U., Cherniack, M., Convey, C., Lee, S., Seidman, G. et al. (2002). 'Monitoring Streams: A New Class of Data Management Applications', In Proceedings of the 28th International Conference on Very Large Data Bases, Hong Kong, China, pp.215-226.
11. Cheng, C. H., Fu, A. W., & Zhang, Y. (1999). 'Entropy Based Subspace Clustering for Mining Numerical Data', In Proceedings of 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.84-93.
12. Domingos, P., & Hulten, G. (2000). 'Mining High- Speed Data Streams', In Proceedings of 6th International Conference on Knowledge Discovery and Data Mining, pp.71-80.
13. Duda, R. O., Hart, P. E., & Stork, D. G. (2000). 'Pattern Classification', John Wiley and Sons
14. Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). 'A Density-based Algorithm for Discovering Clusters in Large Spatial DBs with Noise', In Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining.
15. Gao, J., Li, J., Zhang, Z., & Tan, P. (2005). 'An Incremental Data Stream Clustering Algorithm Based on Dense Units Detection', In Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Hanoi, pp.420-425.
16. Gray, A.,& Moore, A. W. (2003). 'Nonparametric Density Estimation: Towards Computational Trac- tability', In Proceedings of SIAM International Conference on Data Mining.
17. Guha, S., Mishra, N., Motwani, R., & O'Callaghan, L. (2000). 'Clustering Data Streams', In IEEE Symposium on Foundations of Computer Science, pp.259-366.
18. Guha, S., Mishra, N., Motwani, R., & O'Callaghan, L. (2002). 'Streaming-Data Algorithms for High-Quality Clustering', In Proceedings of IEEE International Conference on Data Engineering.
19. Hartigan, J. A. (1975). 'Clustering Algorithms', John Wiley & Sons Inc.
20. Heinz, C., & Seeger, B. (2006). 'Towards Kernel Density Estimation Over Streaming Data', In Proceedings of 13th International Conference on Management of Data(COMAD), Delhi, India
21. Henzinger, M. R., Raghavan, P., & Rajagopalan, S. (1998). 'Computing on Data Streams', Technical Note 1998-011, Digital Systems Research Center, Palo Alto, California.
22. Jain, A. K., Murty, M. N., & Flynn, P. L. (1999). 'Data Clustering: A Review', ACM Computing Survey, pp.264-323.
23. nd workshop on Australasian Information security, Data Mining and Web Intelligence, and Software Internationalization, Dunedin, New Zealand, pp.115-120.
24. Orlowska, M. E., Sun, X., & Li, X. (2006). 'Can Exclusive Clustering on Streaming Data be Achieved', ACM SIGKDD Explorations Newsletter, Vol 8, pp.102-108.
25. Park, N. H., & Lee, W. S. (2004). 'Statistical Gridbased Clustering over Data streams', ACM SIGMOD Record, Volume 33, pp.32-37.
26. Park, N. H., & Lee, W. S. (2007). 'Cell trees: An Adaptive Synopsis Structure for Clustering Multidimensional On-line Data Streams', Data and Knowledge Engineering, Vol 63, pp.528-549.
27. nd International Conference on Advanced Information Networking and Applications, Japan, pp 449-456.
28. Sain, S. R. (1994). 'Adaptive Kernel Density Estimation', PhD Thesis, Rice University.
29. Song, M., & Wang, H. (2004). 'Incremental Estimation of Gaussian Mixture Models for Online Data Stream Clustering', In International Conference on Bioinformatics and its Applications, Hong Kong.
30. Song, M., & Wang, H. (2006). 'Detecting Low Complexity Clusters by Skewness and Kurtosis in Data Stream Clustering', In 9th International Symposium on AI and Maths, Florida.
31. Tasoulis, D. K., Adams, N. M., & Hand, D. J. (2006). 'Unsupervised Clustering in Streaming Data', In Proceedings of 6th IEEE International Conference on Data Mining -Workshops, pp.638-642.UCI KDD Archive, http://kdd.ics.uci.edu.
32. Yang, Q., & Wu, X. (2006). '10 Challenging Problems in Data Mining Research', International Journal of Information Technology & Decision, Vol 5, pp.597-604.
33. Zhang, T., Ramakrishnan, R., & Livny, M. (1996). 'BIRCH: An Efficient Data Clustering Method for Very Large Databases', In Proceedings of the ACM SIGMOD International Conference on Management of Data, Quebec, Canada, pp.103-114.
34. Zhou, A., Cai, Z., Wei, L., & Qian, W. (2003). 'MKernel Merging: Towards Density Estimation Over Data Streams', In Proceedings of 8th International Conference on Database Systems for Advanced Applications (DASFAA), pp.285-292.