Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
2004-06-17
2008-10-07
Cottingham, John (Department: 2167)
Data processing: database and file management or data structures
Database design
Data structure types
C707S793000, C707S793000, C707S793000
Reexamination Certificate
active
07433879
ABSTRACT:
A data processing system processes data sets (such as low-resolution transaction data) into high-resolution data sets by mapping generic information into attribute-based specific information that is stored in a database. The extracting frequent pattern information from the database using frequent pattern growth techniques, a compact frequent pattern tree data structure efficiently holds frequent pattern information for multiple transactions having one or more items in each transaction. Frequent pattern data is transformed for ease of use with rule generation algorithms by removing redundant information (such as part group items) or by consolidating items corresponding to a part group and replacing those items with a proxy item for purposes of power set generation.
REFERENCES:
patent: 6629095 (2003-09-01), Wagstaff et al.
patent: 6697802 (2004-02-01), Ma et al.
patent: 6990486 (2006-01-01), Ma et al.
patent: 2002/0198877 (2002-12-01), Wolff et al.
patent: 2003/0009456 (2003-01-01), Shintani et al.
patent: 2003/0023591 (2003-01-01), Ma et al.
patent: 2003/0078686 (2003-04-01), Ma et al.
patent: 2003/0217055 (2003-11-01), Lee et al.
patent: 2003/0236785 (2003-12-01), Shintani et al.
patent: 2004/0049504 (2004-03-01), Hellerstein et al.
patent: 2005/0027710 (2005-02-01), Ma et al.
patent: 2005/0044073 (2005-02-01), Inokuchi
patent: 2007/0250522 (2007-10-01), Perrizo
U.S. Appl. No. 10/099,404, filed Mar. 15, 2002, Darr et al.
R. Agrawal et al., “Mining Association Rules Between Sets of Items in Large Databases,” Proceedings of ACM SIGMOD Int'l Conference on Management of Data, pp. 207-216 (1993).
J. Han et al., “Mining Frequent Patterns Without Candidate Generation,” Proceedings of ACM SIGMOD Int'l Conference on Management of Data, pp. 1-12 (2000).
J. Han, et al., “Mining Frequent Patterns Without Candidate Generation,” ftp://ftp.fas.sfu.ca/pub/cs/han/slides/alamden00/ppt.
B. Goethals, “Survey on Frequent Pattern Mining,” HIIT Basic Research Unit, Department of Computer Science, University of Helsinki, Finland (2003).
“Association Rule Mining,” COMP 290-90 Seminar, University of North Carolina at Chapel Hill, Fall 2003, http://www.cs.unc.edu/Courses/comp290-90-f03/associationrule1.pdf.
X. Shang, et al., “SQL Based Frequent Pattern Mining Without Candidate Generation” (Poster Abstract) ACM Symposium of Applied Computing, pp. 618-619 (Mar. 2004).
J. Han et al., “Data Mining: Concepts and Techniques,” Department of Computer Science, University of Illinois at Urbana-Champaign, http:// people.sabanciuniv,edu/˜ysaygin/courses/datamining/lecture%20notes.lec1.ppt.
J. Han et al., “Data Mining: Concepts and Techniques,” Department of Computer Science, University of Illinois at Urbana-Champaign, http://people.sabanciuniv.edu/˜ysaygin/courses/datamining/lecture%20notes.lec2.ppt.
J. Han et al., “Data Mining: Concepts and Techniques,” Department of Computer Science, University of Illinois at Urbana-Champaign, http://people.sabanciuniv.edu/˜ysaygin/courses/datamining/lecture%20notes/lec3.ppt.
J. Han et al., “Data Mining: Concepts and Techniques,” Department of Computer Science, University of Illinois at Urbana-Champaign, http://people.sabanciuniv.edu/˜ysaygin/courses/datamining/lecture%20notes.lec4.ppt.
Franke David
Namjoshi Rohit
Sharma Nirad
Arjomandi Noosha
Cannatti Michael Rocco
Cottingham John
Hamilton & Terrile LLP
Versata Development Group, Inc.
LandOfFree
Attribute based association rule mining does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Attribute based association rule mining, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Attribute based association rule mining will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-4009162