Databases Reference
In-Depth Information
7.8
References for Chapter 7
The ancestral study of clustering for large-scale data is the BIRCH Algorithm
of [6]. The BFR Algorithm is from [2]. The CURE Algorithm is found in [5].
The paper on the GRGPF Algorithm is [3].
The necessary background
regarding B-trees and R-trees can be found in [4].
The study of clustering on
streams is taken from [1].
1. B. Babcock, M. Datar, R. Motwani, and L. O'Callaghan, “Maintaining
variance and k-medians over data stream windows,” Proc. ACM Symp.
on Principles of Database Systems, pp. 234-243, 2003.
2. P.S. Bradley, U.M. Fayyad, and C. Reina, “Scaling clustering algorithms
to large databases,” Proc. Knowledge Discovery and Data Mining, pp. 9-
15, 1998.
3. V. Ganti, R. Ramakrishnan, J. Gehrke, A.L. Powell, and J.C. French:,
“Clustering large datasets in arbitrary metric spaces,” Proc. Intl. Conf.
on Data Engineering, pp. 502-511, 1999.
4. H. Garcia-Molina, J.D. Ullman, and J. Widom, Database Systems: The
Complete Book Second Edition, Prentice-Hall, Upper Saddle River, NJ,
2009.
5. S. Guha, R. Rastogi, and K. Shim, “CURE: An e
cient clustering algo-
rithm for large databases,” Proc. ACM SIGMOD Intl. Conf. on Manage-
ment of Data, pp. 73-84, 1998.
6. T. Zhang, R. Ramakrishnan, and M. Livny, “BIRCH: an e
cient data
clustering method for very large databases,” Proc. ACM SIGMOD Intl.
Conf. on Management of Data, pp. 103-114, 1996.
