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Title: US6278997: System and method for constraint-based rule mining in large, dense data-sets
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Country: US United States of America

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27 pages

 
Inventor: Agrawal, Rakesh; San Jose, CA
Bayardo, Roberto Javier; San Jose, CA
Gunopulos, Dimitrios; Riverside, CA

Assignee: International Business Machines Corporation, Armonk, NY
other patents from INTERNATIONAL BUSINESS MACHINES CORPORATION (280070) (approx. 44,393)
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Published / Filed: 2001-08-21 / 1999-02-05

Application Number: US1999000245319

IPC Code: Advanced: G06F 17/30;
Core: more...
IPC-7: G06F 7/00; G06F 17/30;

ECLA Code: G06F17/30S4P8D;

U.S. Class: Current: 707/006;
Original: 707/006;

Field of Search: 707/003,6,505,509

Priority Number:
1999-02-05  US1999000245319

Abstract:     A dense data-set mining system and method is provided that directly exploits all user-specified constraints including minimum support, minimum confidence, and a new constraint, known as minimum gap, which prunes any rule having conditions that do not contribute to its predictive accuracy. The method maintains efficiency even at low supports on data that is dense in the sense that many items appear with high frequency (e.g. relational data).

Attorney, Agent or Firm: Gary Cary Ware & Freidenrich ;

Primary / Asst. Examiners: Breene, John; Pham, Linh M

INPADOC Legal Status: Show legal status actions

Family: None

First Claim:
Show all 48 claims
What is claimed is:     1. A method for mining association rules from a dataset containing data for a plurality of transactions, each transaction having one or more data elements which are related and which have frequently occurring values, the method comprising:
  • generating a set enumeration tree containing one or more nodes wherein each node may represent a group of association rules that satisfy user constraints;
  • pruning groups from the set enumeration tree by selecting groups represented by the set enumeration tree which meet a predetermined set of criteria;
  • processing the groups remaining in the set enumeration tree after pruning to generate a support value for each association rule in each group, said support value indicating a number of transactions in the dataset containing the association rule; and
  • pruning groups from the remaining groups in the set enumeration tree by selecting groups represented by the set enumeration tree based on the predetermined set of criteria to generate association rules.


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Forward References: Show 22 U.S. patent(s) that reference this one

       
U.S. References: Go to Result Set: All U.S. references   |  Forward references (22)   |   Backward references (17)   |   Citation Link

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PDF
Patent  Pub.Date  Inventor Assignee   Title
Buy PDF- 22pp US5615341  1997-03 Agrawal et al.  International Business Machines Corporation System and method for mining generalized association rules in databases
Buy PDF- 8pp US5664171  1997-09 Agrawal et al.  International Business Machines Corporation System and method for query optimization using quantile values of a large unordered data set
Buy PDF- 16pp US5664174  1997-09 Agrawal et al.  International Business Machines Corporation System and method for discovering similar time sequences in databases
Buy PDF- 21pp US5724573  1998-03 Agrawal et al.  International Business Machines Corporation Method and system for mining quantitative association rules in large relational tables
Buy PDF- 11pp US5727199  1998-03 Chen et al.  International Business Machines Corporation Database mining using multi-predicate classifiers
Buy PDF- 10pp US5832482  1998-11 Yu et al.  International Business Machines Corporation Method for mining causality rules with applications to electronic commerce
Buy PDF- 22pp US5870748  1999-02 Morimoto et al.  International Business Machines Corporation Method and apparatus for deriving an optimized confidence rule
Buy PDF- 14pp US5884305  1999-03 Kleinberg et al.  International Business Machines Corporation System and method for data mining from relational data by sieving through iterated relational reinforcement
Buy PDF- 13pp US5920855  1999-06 Aggarwal et al.  International Business Machines Corporation On-line mining of association rules
Buy PDF- 11pp US5943667  1999-08 Aggarwal et al.  International Business Machines Corporation Eliminating redundancy in generation of association rules for on-line mining
Buy PDF- 19pp US5946683  1999-08 Rastogi et al.  Lucent Technologies Inc. Technique for effectively instantiating attributes in association rules
Buy PDF- 12pp US5983222  1999-11 Morimoto et al.  International Business Machines Corporation Method and apparatus for computing association rules for data mining in large database
Buy PDF- 32pp US5991752  1999-11 Fukuda  International Business Machines Corporation Method and apparatus for deriving association rules from data and for segmenting rectilinear regions
Buy PDF- 17pp US6032146  2000-02 Chadha et al.  International Business Machines Corporation Dimension reduction for data mining application
Buy PDF- 13pp US6094645  2000-07 Aggarwal et al.  International Business Machines Corporation Finding collective baskets and inference rules for internet or intranet mining for large data bases
Buy PDF- 22pp US6108004  2000-08 Medl  International Business Machines Corporation GUI guide for data mining
Buy PDF- 19pp US6173280  2001-01 Ramkumar et al.  Hitachi America, Ltd. Method and apparatus for generating weighted association rules
       
Foreign References: None

Other References:
  • "Pincer-Search: A New Algorithm For Discovering the Maximum Frequent Set", Lin et al, Department of Computer Science, New York University.
  • "Mining Association Rules Between Sets of Items In Large Databases", Agrawal et al, Proceedings of the ACM-SIGMOD 1993 Int'l Conference On the Management of Data, Washington, D.C. 1993, pp. 207-216.
  • "An Efficient Algorithm For Mining Association Rules In Large Database", Proceedings of the 21 VLDB Conference, Zurich, Switzerland, 1995, pp. 432-444.
  • "An Effective Hash-Based Algorithm For Mining Association Rules", ACM, 1995, pp. 175-186.
  • "Efficient Parallel Data Mining For Association Rules", Park et al, IBM Research Report, 26 pages, R 20156, Aug. 1995.
  • "Set-Oriented Mining For Association Rules In Relational Databases", Houtsma et al, 11th Conference on Data Engineering, Mar. 6-10, 1995, Taipei, Taiwan, pp. 25-33.
  • "Mining Generalized Association Rules", Srikant et al, Proceedings of the 21st VLDB Conference, Zurich, Switzerland, 1995, pp. 407-419.
  • "An Information Theoretic Approach To Rule Induction From Databases", IEEE Transactions on Knowledge And Data Engineering, vol. 4, No. 4, Aug. 1992, pp. 301-316. (16 pages) Cited by 8 patents [ISI abstract]
  • "Dynamic Itemset Counting and Implication Rules For Market Basket Data", ACM 0-89797-911.4, 1997, pp. 255-264.
  • "Brute-Force Mining of High Confidence Classification Rules", Bayardo, American Association for Artificial Intelligence, 1997.
  • "Discovering all Most Specific Sentences By Randomized Algorithms Extended Abstract", Gunopulos et al, ICDT, 1997.
  • "Efficiently Mining Long Patterns From Databses", R. J. Bayardo, Jr., To appear in Proc. of the 1998 ACM-SIGMOD Conference on Management of Data.
  • "New Algorithms For Fast Discovery of Association Rules", Zaki et al, American Association for Artificial Intelligence, 1997, pp. 283-286.
  • "Fast Algorithms For Mining Association Rules", Agrawal et al, Proceedings of the 20th VLDB Conference Santiago, Chile 1994, 487-499.
  • "Database Mining: A Performance Perspective", Agrawal et al, IEEE Transactions on Knowledge And Data Engineering, vol. 5, No. 6, Dec. 1993, pp. 914-925. (12 pages) Cited by 35 patents [ISI abstract]
  • "Mining Sequential Patterns", Proc. Of the Int'l Conference on Data Engineering, Taipei, Taiwan, 1995, pp. 3-14.
  • "Discovery of Multiple-Level Association Rules From Large Databases", Han et al, Proceedings of the 21st International Conference on Very Large Data Bases, Zurich, Switzerland, Sept. 11-15, 1995, pp. 420-431.
  • "Improved Methods for Finding Association Rules", Mannila et al, Pub. No. C-1993-65, pp. 1-20, University Helsinki, Dec. 1993.


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