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tail data set. This research was supported in part by Intel, Accenture, and
IBM/Tivoli.
References
3.1Lawrenc, R. D., et al, Personalization of supermarket product recommenda-
tions, Data Mining and Knowledge Discovery, 4(1/2):11-32, 2001.
3.2Strehl,A., and Ghosh, J., Relationship-based clustering and visualization for
high-dimensional data mining, INFORMS Journal on Computing, 15(2):208-
30, 2003.
3.3 Banerjee, A., and Ghosh, J., Frequency sensitive competitive learning for
clustering on high-dimensional hyperspheres, in Proc. IJCNN'02, Honolulu,
pp. 1590-5, May 2002.
3.4 Gupta, G. K., and Ghosh, J., Detecting seasonal trends and cluster motion
visualization for very high dimensional transactional data, in Proc. 1st SIAM
Conf. on Data Mining (SDM2001),pp. 115-29, 2001.
3.5 Zipf, G. K.,Relative frequency as a determinant of phonetic change, Reprinted
from the Harvard Studies in Classical Philiology, XL, 1929.
3.6 Berry, M.J.A., and Linoff,G., Data Mining Techniques for Marketing, Sales
and Customer Support, Wiley, 1997.
3.7 Guha, S., Rastogi, R., and Shim, K.,ROCK:Arobust clustering algorithm for
categorical attributes, Information Systems, 25(5):345-66, 2000.
3.8Han,J., and Kamber, M., Data Mining: Concepts and Techniques, Morgan
Kaufmann, 2001.
3.9Karypis,G.,Han,E.-H., and Kumar, V., Chameleon: Hierarchical clustering
using dynamic modeling, IEEE Computer, 32(8):68-75, Aug. 1999.
3.10 Pekalska, E., Paclik, P., and Duin, R. P.W., A generalized kernel approach
to dissimilarity-based classification, Journal of Machine Learning Research,
Special Issue on Kernel Methods,2(2):175-211, 2002.
3.11 Karypis, G., and Kumar, V., A fast and high quality multilevel scheme for par-
titioning irregular graphs, SIAM Journal on Scientific Computing, 20(1):359-
92, 1998.
3.12 Amari, S.-I., The EM algorithm and information geometry in neural network
learning, Neural Computation,7:13-8, 1995.
3.13 Strehl, A.,Ghosh,J., and Mooney, R., Impact of similarity measures on web-
page clustering, in Proc. 17th National Conference on AI: Workshop on AI for
Web Search (AAAI 2000),pp. 58-64, AAAI, July 2000.
3.14 Jain, A. K., and Dubes, R. C., Algorithms for Clustering Data, Prentice Hall,
NewJersey, 1988.
3.15 Garey, M. R.,andJohnson, D. S., Computers and Intractability: A Guide to
the Theory of NP-Completeness, W. H. Freeman, San Francisco, CA, 1979.
3.16 Kernighan, B., and Lin, S., Ane
cient heuristic procedure for partitioning
graphs, Bell Systems Technical Journal,49:291-307, 1970.
3.17 Pothen, A., Simon, H.,andLiou,K., Partitioning sparse matrices with eigen-
vectors of graphs, SIAM Journal of Matrix Analysis and Applications,11:430-
52, 1990.
3.18 Hendrickson, B., and Leland, R., An improved spectral graph partitioning al-
gorithm for mapping parallel computations, SIAM Journal on Scientific Com-
puting, 16(2):452-69, 1995.
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