Data processing: structural design – modeling – simulation – and em – Modeling by mathematical expression
Reexamination Certificate
2005-09-20
2005-09-20
Paladini, Albert W. (Department: 2125)
Data processing: structural design, modeling, simulation, and em
Modeling by mathematical expression
C707S793000
Reexamination Certificate
active
06947878
ABSTRACT:
A computer-implemented data mining system that analyzes data using Gaussian Mixture Models. The data is accessed from a database, and then an Expectation-Maximization (EM) algorithm is performed in the computer-implemented data mining system to create the Gaussian Mixture Model for the accessed data. The EM algorithm generates an output that describes clustering in the data by computing a mixture of probability distributions fitted to the accessed data.
REFERENCES:
patent: 5787425 (1998-07-01), Bigus
patent: 5909681 (1999-06-01), Passera et al.
patent: 6263337 (2001-07-01), Fayyad et al.
patent: 6581058 (2003-06-01), Fayyad et al.
patent: 6591235 (2003-07-01), Chen et al.
patent: 6816848 (2004-11-01), Hildreth et al.
C. Aggrawal et al., “Fast Algorithms for Projected Clustering,” In Proceedings of the ACM SIGMOD Int'l Conf on Management of Data, Philadelphia, PA, 1999.
R. Agrawal et al., “Automatic Subspace Clustering of High . . . Applications,” In Proceedings of ACM SIGMOD Int'l Conf on Management of Data, Seattle, WA, 1998.
H. Bozdogan, “Model selection and Akaike's information criterion . . . extensions,” Psychometrika, 52(3):345-370, 1987.
P.S. Bradley et al., “Scaling Clustering Algorithms to Large Databases,” In Proceedings of the Int'l Knowledge Discovery and Data Mining Conference (KDD), 1998.
P.S. Bradley eet al., “Scaling EM (Expectation-Maximization) Clustering to Large Databases,” Microsoft Research Technical Report, 20 pages, 1998.
A.P. Dempster et al., “Maximum Likelihood from Incomplete Data via the EM Algorithm,” Journal of The Royal Statistical Society, 39(1):1-38, 1977.
M. Ester et al., “A Density-Based Algorithm for Discovering . . . Noise,” In Proceedings of the IEEE, Int'l Conf on Data Engineering (ICDE), Portland, Oregon, 1996.
G. Graefe et al., “On the Efficient Gathering . . . Databases,” Microsoft, AAAI, 5 pages, 1998.
A. Hinneburg et al., “Optimal Grid-Clustering. Towards Breaking the Curse . . . Clustering,” In Proceedings of the 25thInt'l Conf on Very Large Data Bases, Edinburgh, Scotland, 1999.
M.I. Jordan et al., “Hierarchical Mixtures of Experts and the EM Algorithm,” Neural Computation, 6:181-214, 1994.
F. Murtagh, “A Survey of Recent Advances in Hierarchical Clustering Algorithms,” The Computer, Journal, 26(4):354-359, 1983.
R.T. Ng et al., “Efficient and Effective Clustering Methods . . . Mining,” In Proc. of the VLDB Conf, Santago, Chile, 1994.
W.H. Press et al., “Numerical Recipes in C,” Cambridge University Press: Cambridge, 20 pgs., 1986.
S. Roweis, “A Unifying Review of Linear Gaussian Models,” Neural Computation, 11:305-345, 1999.
T. Zhang et al., “BIRCH: An Efficient Data Clustering . . . Databases,” Int'l Proc of the ACM SIGMOD Conference, Montreal, Canada, pp. 103-114, 1996.
A White Paper Prepared by MciroStrategy, Inc., “The Case for Relational OLAP,” 20 pages, 1995.
Bisgaard-B hr Mikael
Cunningham Scott Woodroofe
Gates & Cooper LLP
NCR Corporation
Paladini Albert W.
Stover James M.
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