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 Title: |
US6003029:
Automatic subspace clustering of high dimensional data for data mining applications
[ Derwent Title ]
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| Country: |
US United States of America
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| Inventor: |
Agrawal, Rakesh; San Jose, CA
Gehrke, Johannes Ernst; Madison, WI
Gunopulos, Dimitrios; San Jose, CA
Raghavan, Prabhakar; Saratoga, CA
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| Assignee: |
International Business Machines Corporation, Armonk, NY
other patents from INTERNATIONAL BUSINESS MACHINES CORPORATION (280070) (approx. 44,393)
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| Published / Filed: |
1999-12-14
/ 1997-08-22
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| Application Number: |
US1997000916347
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| IPC Code: |
Advanced:
G06F 17/30;
G06K 9/62;
Core:
more...
IPC-7:
G06F 17/30;
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| ECLA Code: |
G06F17/30S8R1; G06F17/30S8T; G06K9/62B1;
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| U.S. Class: |
Current:
707/007;
707/001;
707/006;
Original:
707/007;
707/001;
707/006;
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| Field of Search: |
707/007,1,6
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| Priority Number: |
| 1997-08-22 |
US1997000916347 |
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| Abstract: |
A method for finding clusters of units in high-dimensional data having the steps of determining dense units in selected subspaces within a data space of the high-dimensional data, determining each cluster of dense units that are connected to other dense units in the selected subspaces within the data space, determining maximal regions covering each cluster of connected dense units, determining a minimal cover for each cluster of connected dense units, and identifying the minimal cover for each cluster of connected dense units.
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| Attorney, Agent or Firm: |
Tran, Esq., Khanh Q.Banner & Witcoff, Ltd. ;
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| Primary / Asst. Examiners: |
Black, Thomas G.; Coby, Frantz
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| Maintenance Status: |
E1 Expired Check current status
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| INPADOC Legal Status: |
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| Parent Case: |
CROSS-REFERENCE TO RELATED APPLICATIONS
The present application is related to an application entitled "Discovery-Driven Exploration Of OLAP Data Cubes," by Sunita Sarawagi and Rakesh Agrawal, Ser. No. 08/916,346 filed on Aug. 22, 1997, having common ownership, filed concurrently with the present application, and incorporated by reference herein.
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| Family: |
None
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First Claim:
Show all 32 claims |
What is claimed is:
1. A method for finding clusters of units in high-dimensional data in a database, the method comprising the steps of:
- determining dense units in selected subspaces within a data space of high-dimensional data in a database;
- determining each cluster of dense units that are connected to other dense units in the selected subspaces within the data space;
- determining maximal regions covering each cluster of connected dense units; and
- determining a minimal cover for each cluster of connected dense units.
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| Background / Summary: |
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| Drawing Descriptions: |
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| Description: |
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| Forward References: |
Show 57 U.S. patent(s) that reference this one
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| Foreign References: |
None
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| Other Abstract Info: |
DERABS G2000-180975
DERABS G2000-180975
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| Other References: |
R. Agrawal et al., Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications, Paper AR-- 297, pp. 1-18, 1997.
R. Agrawal et al., Modeling Multidemsional Databases, Procedings of the 13th International Conference on Data Engineering, pp. 232-234, Birmingham, England, Apr., 1997.
P.J. Rousseeuw et al., Robust Regression and Outlier Detection, John Wiley & Sons, pp. 216-229, 1987.
A. Arning et al., A Linear Method for Deviation Detection in Large Databases, Proceedings of the 2nd International Conference on Knowledge Discovery in Databases and Data Mining, pp. 164-169, Portland, Oregon, Aug., 1996.
C.J. Matheus et al., Selecting and Reporting What is Interesting, Advances in Knowledge Discovery and Data Mining, pp. 495-515, AAAI Press, 1996.
W. Klosgen, Efficient Discovery of Interesting Statements in Databases, Journal of Intelligent Information Systems (JIIS), vol. 4, No. 1, pp. 53-69, Jan. 1995.
D.C. Montgomery, Design and Anaylsis of Experiments, Third Edition, John Wiley & Sons, pp. 196-215, and pp. 438-455, 1991.
S. Agarwal et al., On the Computation of Multidimensional Aggregates, Proceedings of the 22nd VLDB Conference Mumbai (Bombay), India, 1996, pp. 1-16.
R. Agrawal et al., An Interval Classifier for Database Mining Applications, Proceedings of the 18th VLDB Conference, Vancouver, British Columbia, Canada, 1992, pp. 1-14.
R. Agrawal et al. Database Mining: A Performance Perspective, IEEE Transactions on Knowledge and Data Engineering, vol. 5, No. 6, Dec. 1993, pp. 914-925.
(12 pages)
Cited by 35 patents
[ISI abstract]
L.G. Valiant, A Theory of the Learnable, Communications of the ACM, vol. 27, pp. 1134-1142, 1984.
(9 pages)
Cited by 3 patents
D.E. Rumelhart et al., Feature Discovery by Competitive Learning, originally published in Cognitive Science, 9:1, 1965, pp. 306-325.
R.S. Michalski et al. Learning from Observation: Conceptual Clustering, Machine Learning: An Artificial Approach, vol. I, R.S. Michalski et al. (Editors), Morgan-Kaufman, pp. 331-363, 1983.
S. Jose, Conceptual Clustering, Categorization, and Polymorphy; Machine Learning 3: 343-372; Copyright 1989 Kluwer Academic Publishers.
D.H. Fisher, Knowledge Acquisition Via Incremental Conceptual Clustering; pp. 267-283; Originally published in Machine Learning, copyright 1987 Kluwer Academic Publishers, Boston.
D.W. Aha et al., Instance-Based Learning Algorithms; Machine Learning, 6, 37-66 copyright 1991 Kluwer Academic Publishers, Boston.
(30 pages)
Cited by 18 patents
[ISI abstract]
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