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Title: US6324533: Integrated database and data-mining system
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Country: US United States of America

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28 pages

 
Inventor: Agrawal, Rakesh; San Jose, CA
Sarawagi, Sunita; San Jose, CA
Thomas, Shiby; Gainesville, FL

Assignee: International Business Machines Corporation, Armonk, NY
other patents from INTERNATIONAL BUSINESS MACHINES CORPORATION (280070) (approx. 44,393)
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Published / Filed: 2001-11-27 / 1998-05-29

Application Number: US1998000087561

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

ECLA Code: G06F17/30S4P8D;

U.S. Class: Current: 707/003; 706/047;
Original: 707/003; 706/047;

Field of Search: 707/001,3,4,6 705/010 706/047,46

Priority Number:
1998-05-29  US1998000087561

Abstract:     A method and apparatus for mining data relationships from an integrated database and data-mining system are disclosed. A set of frequent 1-itemsets is generated using a group-by query on data transactions. From these frequent 1-itemsets and the transactions, frequent 2-itemsets are determined. A candidate set of (n+2)-itemsets are generated from the frequent 2-itemsets, where n=1. Frequent (n+2)-itemsets are determined from candidate set and the transaction table using a query operation. The candidate set and frequent (n+2)-itemset are generated for (n+1) until the candidate set is empty. Rules are then extracted from the union of the determined frequent itemsets.

Attorney, Agent or Firm: Tran, Khanh Q. ;

Primary / Asst. Examiners: Black, Thomas G.; Le, Uyen

INPADOC Legal Status: Show legal status actions

Family: None

First Claim:
Show all 33 claims
What is claimed is:     1. A method for mining rules from an integrated database and data-mining system having a table of data transactions and a query engine, the method comprising the steps of:
  • a) performing a group-by query on the transaction table to generate a set of frequent 1-itemsets;
  • b) determining frequent 2-itemsets from the frequent 1-itemsets and the transaction table;
  • c) generating a candidate set of (n+2)-itemsets from the frequent (n+1)-itemsets, where n=1;
  • d) determining frequent (n+2)-itemsets from the candidate set of (n+2)-itemsets and the transaction table using a query operation;
  • e) repeating steps (c) and (d) with n=n+1 until the candidate set is empty; and
  • f) generating rules from the union of the determined frequent itemsets.


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Forward References: Show 30 U.S. patent(s) that reference this one

       
U.S. References: Go to Result Set: All U.S. references   |  Forward references (30)   |   Backward references (7)   |   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- 10pp US5832482  1998-11 Yu et al.  International Business Machines Corporation Method for mining causality rules with applications to electronic commerce
Buy PDF- 20pp US5970464  1999-10 Apte et al.  International Business Machines Corporation Data mining based underwriting profitability analysis
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
       
Foreign References: None

Other References:
  • Tang et al., "Using Incremental Pruning to Increase the Efficiency of Dynamic Itemset Counting for Mining Asssociation Rules", ACM 1998.*
  • Srikant et al, "Mining Quantitative Association Rules in Large Relational Tables", ACM 1996.*
  • Sarawagi et al, "Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications", ACM 1998, pp. 343-354.*
  • R. Agrawal et al., "Data 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]
  • R. Agrawal et al., "Developing Tightly-Coupled Data Mining Applications on a Relational Database System," Proceedings of the 2nd Int'l Conference on Knowledge Discovery in Databases and Data Mining, Oregon, Aug. 1996.
  • R. Agrawal et al., "Fast Algorithms for Mining Association Rules," Proceedings of the 20th VLDB Conference, Santiago, Chile, 1994, pp. 487-499.
  • R. Agrawal et al., "Mining Association Rules between Sets of Items in Large Databases," Proceedings of the ACM-SIGMOD 1993 Int'l Conference on the Management of Data, Washington, DC, 1993, pp. 207-216.
  • R. Agrawal et al., "Mining Sequential Patterns," Proceedings of the Int'l Conference on Data Engineering, Taipei, Taiwan, 1995, pp. 3-14.
  • Bayardo, Jr., "Brute-Force Mining of High-Conference Classification Rules," Proceedings of the 3rd Int'l Conference on Knowlege Discovery and Data Mining, California, 1997.
  • S. Brin et al., "Dynamic Itemset Counting and Implication Rules for Market Basket Data," Proceedings of the ACM SIGMOD Conference on Management of Data, 1997.
  • J. Han, "Discovery of Muliple-Level Association Rules from Large Databases," Proceedings of the 21st VLBD Conference, Zurich, Switzerland, 1995, pp. 420-431.
  • M. Houtsma, "Set-Oriented Mining for Assocation Rules in Relational Databases," Proc. of the Int'l Conference on Data Engineering, Taipei, Taiwan, 1995, pp. 25-33.
  • T. Imielinski et al., "A Database Perspective on Knowledge Discovery," Communication of the ACM, vol. 39, No. 11, Nov. 1996, pp. 58-64. (7 pages) Cited by 2 patents [ISI abstract]
  • D. Lin et al., "Pincer-Search: A New Algorithm for Discovering the Maximum Frequent Set," EDBT '98, Jun., 1998.
  • H. Mannila et al., "Improved Methods for Finding Association Rules," Pub. No. C-1993-65, University of Helsinki, 1993, pp. 1-20.
  • R. Meo et al., "A New SQL-like Operator for Mining Association Rules," Proceedings of the 22nd VLDB Conference Mumbai (Bombay), India, 1996.
  • J.S. Park et al., "An Effective Hash-Based Algorithm for Mining Association Rules," Proceedings of the 1995 ACM SIGMOD Conference, San Jose, California, 1995, pp. 175-186.
  • J.S. Park et al., "Efficient Parallel Data Mining for Association Rules," IBM Research Report, RJ20156, Aug., 1995, 26 pages.
  • S. Sarawagi et al., "Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications," IBM Research Report, RJ10107, Mar., 1998, 31 pages.
  • A. Savasere et al., "An Efficient Algorithm for Mining Association Rules in Large Databases," Proceedings of the 21st VLDB Conference, Zurich, Switzerland, 1995, pp. 432-444.
  • A. Siebes et al., "KESO: Minimizing Database Interaction," Proceedings of the 3rd Int'l Conference on Knowledge Discovery and Data Mining, California, 1997, pp. 247-250.
  • R. Srikant et al., "Mining Association Rules with Item Constraints," Proceedings of the 3rd Int'l Conference on Knowledge Discovery in Databases and Data Mining, Oregon, 1996, pp. 67-73.
  • R. Srikant et al., "Mining Sequential Patterns: Generalizations and Performance Improvements," IBM Research Report, RJ9994, Dec. 1995. Also in Proceedings of the 5th Conference on Extending Database Technology (EDBT) Avignon, France, 1996.
  • M.J. Zaki et al., "New Algorithms for Fast Discovery of Association Rules," Proceedings of the 3rd Int'l Conference on Knowledge Discovery and Data Mining, California, Aug. 1997, pp. 283-286.
  • "Advances in Knowledge Discovery and Data Mining," edited by U. M. Fayyad et al., AAAI Press / The MIT Press, Copyright 1996.


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