Computer graphics processing and selective visual display system – Computer graphics processing – Graph generating
Patent
1998-06-22
2000-08-08
Powell, Mark R.
Computer graphics processing and selective visual display system
Computer graphics processing
Graph generating
G06T 1120
Patent
active
061009016
ABSTRACT:
A method and apparatus for visualizing a multi-dimensional data set in which the multi-dimensional data set is clustered into k clusters, with each cluster having a centroid. Then, either two distinct current centroids or three distinct non-collinear current centroids are selected. A current 2-dimensional cluster projection is generated based on the selected current centroids. In the case when two distinct current centroids are selected, two distinct target centroids are selected, with at least one of the two target centroids being different from the two current centroids. In the case when three distinct current centroids are selected, three distinct non-collinear target centroids are selected, with at least one of the three target centroids being different from the three current centroids. An intermediate 2-dimensional cluster projection is generated based on a set of interpolated centroids, with each interpolated centroid corresponding to a current centroid and to a target centroid associated with the current centroid. Each interpolated centroid is interpolated between the corresponding current centroid and the target centroid associated with the current centroid. Alternatively, the intermediate 2-dimensional cluster projection is generated based on an interpolated 2-dimensional nonlinear cluster projection that is based on the selected current centroids and the selected target centroids.
REFERENCES:
patent: 5450535 (1995-09-01), North
patent: 5625767 (1997-04-01), Bartell et al.
patent: 5832182 (1998-11-01), Zhang et al.
patent: 5983224 (1999-11-01), Singh et al.
Lee et al., "Modified K-means Algorithm for Vector Quantizer Design", IEEE Signal Processing Letters, vol. 4, No. 1, pp. 2-4, Jan. 1997.
Su et al, "Application of Neural Networks in Cluster Analysis", IEEE, pp. 1-5, Jan. 1997.
D. Keim, Enhancing the Visual Clustering of Query-Dependent Database Visualization Techniques Using Screen-Filling Curves, Institute for Computer Science, University of Munich, Leopoldstr. 11B, D-80802, Munich, Germany.
G. Grinstein et al., Visualizing Multidimensional (Multivariate) Data and Relations, Proceedings of the IEEE Conference on Visualization 1994, Washington, D.C., pp. 404-411.
M. Ester et al., Spatial Data Mining: A Database Approach, Advances in Spatial Databases, M. Scholl et al. Editors, 5th International Symposium, SSD'97 Berlin, Germany, Jul. 15-18, 1997 Proceedings, pp. 46-66.
D.P. Huttenlocher et al., Comparing Point Sets Under Projection, Proceedings of the Fifth Annual ACM-SIAM Symposium On Discrete Algorithms, Arlington, Virginia, Jan. 23-25, 1994; pp. 1-7.
S.Z. Selim et al., K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-6, No. 1, Jan. 1984, pp. 81-87.
J. H. Friedman et al., A Projection Pursuit Algorithm for Exploratory Data Analysis, IEEE Transactions On Computers, vol. c-23, No. 9, Sep. 1974, pp. 881-810.
H. Ralambondrainy, A Conceptual Version of the K-Means Algorithm. Pattern Recognition Letters 16 (1995) pp. 1147-1157.
S. Vaithyanathan et al., A Multiple PCA (MPCA) Model for Hierarchical Decomposition Of Large Document Collections, IBM Research Report, RJ 10082 (91898) Jul. 25, 1997, Computer Science, pp. 1-13.
D. Swayne et al., Exploratory Data Analysis Using Interactive Dynamic Graphics, Third International Conference on Knowledge Discovery & Data Mining, Newport Beach, California, Aug. 14, 1997, pp. T4-1, T4-4-T4-32.
A. Buja et al., Theory and Computational Methods for Dynamic Projections in High-Dimensional Data Visualization, Journal of Computational and Graphical Statistics, 1997, pp. 1-67.
G.T. Toussaint, The Relative Neighbourhood Graph Of A Finite Planar Set, Pattern Recognition, vol. 12, pp. 261-268, received Sept. 21 1979.
M. Ichino, The Relative Neighborhood Graph For Mixed Feature Variables, Pattern Recognition, vol. 18, No. 2, pp. 161-167, 1985.
G.W. Furnas et al., Prosection Views: Dimensional Inference Through Sections and Projections, Journal of Computational and Graphical Statistics, pp. 1-26, Dec. 1, 1993.
A. Buja et al., Grand Tour Methods: An Outline, Elsevier Science Publishers B.V. (North-Holland), pp. 63-67, 1986.
D. Pollard, A Central Limit Theorem for k-Means Clustering, The Annals of Probability, vol. 10, No. 4, 919-926, 1982.
D. Pollard, Quantization and the Method of k-Means, IEEE Transactions on Information Theory, vol. IT-28, No. 2, pp. 199-205, Mar. 1982.
C. Hurley et al., Analyzing High-Dimensional Data With Motion Graphics, SIAM Journal of Science Stat. Computer, vol. 11, No. 6, pp. 1193-1211, Nov 1990.
D. Asimov, The Grand Tour: A Tool For viewing Multidimensional Data, SIAM Journal of Science Stat. Computer, vol. 6, No.1, pp. 128-143, Jan. 1985.
Duda et al., Unsupervised Learning And Clustering, Pattern classification and Scene Analysis, New York, Wiley, pp. 210-257, 1973.
B. Ripley, Pattern Recognition and Netural Networks, Cambridge University Press, pp. 311-322, 1996.
Martin David Charles
Mohda Dharmendra Shantilal
Spangler William Scott
Vaithyanathan Shivakumar
Good-Johnson Motilewa
International Business Machines - Corporation
Powell Mark R.
Tran Khank Q.
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