Databases Reference
In-Depth Information
[GRS99]
S. Guha, R. Rastogi, and K. Shim. ROCK: A robust clustering algorithm for categorical
attributes. In Proc. 1999 Int. Conf. Data Engineering (ICDE'99) , pp. 512-521, Sydney,
Australia, Mar. 1999.
[Gru69]
F. E. Grubbs. Procedures for detecting outlying observations in samples. Technometrics ,
11:1-21, 1969.
[Gup97]
H. Gupta. Selection of views to materialize in a data warehouse. In Proc. 7th Int. Conf.
Database Theory (ICDT'97) , pp. 98-112, Delphi, Greece, Jan. 1997.
[Gut84]
A. Guttman. R-Tree: A dynamic index structure for spatial searching. In Proc. 1984
ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'84) , pp. 47-57, Boston, MA,
June 1984.
[GW07]
R. C. Gonzalez and R. E. Woods. Digital Image Processing (3rd ed.). Prentice Hall, 2007.
[GZ03a]
B. Goethals and M. Zaki. An introduction to workshop frequent itemset mining imple-
mentations. In Proc. ICDM'03 Int. Workshop Frequent Itemset Mining Implementations
(FIMI'03) , pp. 1-13, Melbourne, FL, Nov. 2003.
[GZ03b]
G. Grahne and J. Zhu. Efficiently using prefix-trees in mining frequent itemsets. In
Proc. ICDM'03 Int. Workshop on Frequent Itemset Mining Implementations (FIMI'03) ,
Melbourne, FL, Nov. 2003.
[HA04]
V. J. Hodge, and J. Austin.
A survey of outlier detection methodologies.
Artificial
Intelligence Review , 22:85-126, 2004.
[HAC C 99]
J. M. Hellerstein, R. Avnur, A. Chou, C. Hidber, C. Olston, V. Raman, T. Roth, and P. J.
Haas. Interactive data analysis: The control project. IEEE Computer , 32:51-59, 1999.
[Ham94]
J. Hamilton. Time Series Analysis . Princeton University Press, 1994.
[Han98]
J.
Han.
Towards
on-line
analytical
mining
in
large
databases.
SIGMOD
Record ,
27:97-107, 1998.
[Har68]
P. E. Hart. The condensed nearest neighbor rule. IEEE Trans. Information Theory ,
14:515-516, 1968.
[Har72]
J. Hartigan. Direct clustering of a data matrix. J. American Stat. Assoc. , 67:123-129, 1972.
[Har75]
J. A. Hartigan. Clustering Algorithms . John Wiley & Sons, 1975.
[Hay99]
S. S. Haykin. Neural Networks: A Comprehensive Foundation . Prentice-Hall, 1999.
[Hay08]
S. Haykin. Neural Networks and Learning Machines . Prentice-Hall, 2008.
[HB87]
S. J. Hanson and D. J. Burr. Minkowski-r back-propagation: Learning in connection-
ist models with non-euclidian error signals. In Neural Information Proc. Systems Conf. ,
pp. 348-357, Denver, CO, 1987.
[HBV01]
M. Halkidi, Y. Batistakis, and M. Vazirgiannis. On clustering validation techniques.
J. Intelligent Information Systems , 17:107-145, 2001.
[HCC93]
J. Han, Y. Cai, and N. Cercone. Data-driven discovery of quantitative rules in relational
databases. IEEE Trans. Knowledge and Data Engineering , 5:29-40, 1993.
[HCD94]
L. B. Holder, D. J. Cook, and S. Djoko. Substructure discovery in the subdue system. In
Proc. AAAI'94 Workshop on Knowledge Discovery in Databases (KDD'94) , pp. 169-180,
Seattle, WA, July 1994.
[Hec96]
D. Heckerman. Bayesian networks for knowledge discovery. In U. M. Fayyad,
G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (eds.), Advances in Knowledge
Discovery and Data Mining , pp. 273-305. Cambridge, MA: MIT Press, 1996.
 
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