ACM SIGMOD Anthology TKDE dblp.uni-trier.de

Data-Driven Discovery of Quantitative Rules in Relational Databases.

Jiawei Han, Yandong Cai, Nick Cercone: Data-Driven Discovery of Quantitative Rules in Relational Databases. IEEE Trans. Knowl. Data Eng. 5(1): 29-40(1993)
@article{DBLP:journals/tkde/HanCC93,
  author    = {Jiawei Han and
               Yandong Cai and
               Nick Cercone},
  title     = {Data-Driven Discovery of Quantitative Rules in Relational Databases},
  journal   = {IEEE Trans. Knowl. Data Eng.},
  volume    = {5},
  number    = {1},
  year      = {1993},
  pages     = {29-40},
  ee        = {db/journals/tkde/HanCC93.html},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

A quantitative rule is a rule associated with quantitative information which assesses the representativeness of the rule in the database. In this paper, we develop an efficient induction method for learning quantitative rules in relational databases. With the assistance of knowledge about concept hierarchies, data relevance, and expected rule forms, attribute-oriented induction can be performed on the database, which integrates database operations with the learning process and provides a simple, efficient way of learning quantitative rules from large databases. Our method learns both characteristic rules and classification rules. Quantitative information facilitates quantitative reasoning, incremental learning, and learning in the presence of noise. Moreover, learning qualitative rules can be treated as a special case of learning quantitative rules. Our paper shows that attribute-oriented induction provides an efficient and effective mechanism for learning various kinds of knowledge rules from relational databases.

Copyright © 1993 by The Institute of Electrical and Electronic Engineers, Inc. (IEEE). Abstract used with permission.

Joint ACM SIGMOD / IEEE Computer Society Anthology

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References

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BibTeX
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Contents BibTeX

Referenced by

  1. Wen-Chi Hou: A Framework for Statistical Data Mining with Summary Tables. SSDBM 1999: 14-23
  2. Colin L. Carter, Howard J. Hamilton: Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large Databases. IEEE Trans. Knowl. Data Eng. 10(2): 193-208(1998)
  3. Jiawei Han: Towards On-Line Analytical Mining in Large Databases. SIGMOD Record 27(1): 97-107(1998)
  4. Martin Ester, Hans-Peter Kriegel, Jörg Sander, Michael Wimmer, Xiaowei Xu: Incremental Clustering for Mining in a Data Warehousing Environment. VLDB 1998: 323-333
  5. Chien-Le Goh, Masahiko Tsukamoto, Shojiro Nishio: Fast Methods with Magic Sampling for Knowledge Discovery in Deductive Databases with Large Deduction Results. ER Workshops 1998: 14-28
  6. Martin Ester, Rüdiger Wittmann: Incremental Generalization for Mining in a Data Warehousing Environment. EDBT 1998: 135-149
  7. Sanjay Goil, Alok N. Choudhary: High Performance Multidimensional Analysis of Large Datasets. DOLAP 1998: 34-39
  8. Renée J. Miller, Yuping Yang: Association Rules over Interval Data. SIGMOD Conference 1997: 452-461
  9. Jiawei Han, Krzysztof Koperski, Nebojsa Stefanovic: GeoMiner: A System Prototype for Spatial Data Mining. SIGMOD Conference 1997: 553-556
  10. David Wai-Lok Cheung, Sau Dan Lee, Ben Kao: A General Incremental Technique for Maintaining Discovered Association Rules. DASFAA 1997: 185-194
  11. Daniel A. Keim, Hans-Peter Kriegel: Visualization Techniques for Mining Large Databases: A Comparison. IEEE Trans. Knowl. Data Eng. 8(6): 923-938(1996)
  12. Yue-Ming Huan, Shian-Hua Lin: An Efficient Inductive Learning Method for Object-Oriented Database Using Attribute Entropy. IEEE Trans. Knowl. Data Eng. 8(6): 946-951(1996)
  13. Jiawei Han, Yue Huang, Nick Cercone, Yongjian Fu: Intelligent Query Answering by Knowledge Discovery Techniques. IEEE Trans. Knowl. Data Eng. 8(3): 373-390(1996)
  14. Chien-Le Goh, Masahiko Tsukamoto, Shojiro Nishio: Knowledge Discovery in Deductive Databases with Large Deduction Results: the First Step. IEEE Trans. Knowl. Data Eng. 8(6): 952-956(1996)
  15. David Wai-Lok Cheung, Vincent T. Y. Ng, Ada Wai-Chee Fu, Yongjian Fu: Efficient Mining of Association Rules in Distributed Databases. IEEE Trans. Knowl. Data Eng. 8(6): 911-922(1996)
  16. Ming-Syan Chen, Jiawei Han, Philip S. Yu: Data Mining: An Overview from a Database Perspective. IEEE Trans. Knowl. Data Eng. 8(6): 866-883(1996)
  17. Jiawei Han, Yongjian Fu, Wei Wang, Jenny Chiang, Osmar R. Zaïane, Krzysztof Koperski: DBMiner: Interactive Mining of Multiple-Level Knowledge in Relational Databases. SIGMOD Conference 1996: 550
  18. David Wai-Lok Cheung, Jiawei Han, Vincent T. Y. Ng, C. Y. Wong: Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique. ICDE 1996: 106-114
  19. Jiawei Han, Yongjian Fu: Discovery of Multiple-Level Association Rules from Large Databases. VLDB 1995: 420-431
  20. Show-Jane Yen, Arbee L. P. Chen: An Efficient Algorithm for Deriving Compact Rules from Databases. DASFAA 1995: 364-371
  21. Jiawei Han: Mining Knowledge at Multiple Concept Levels. CIKM 1995: 19-24
  22. Jiawei Han, Yongjian Fu, Yue Huang, Yandong Cai, Nick Cercone: DBLearn: A System Prototype for Knowledge Discovery in Relational Databases. SIGMOD Conference 1994: 516
  23. Jiawei Han, Yandong Cai, Nick Cercone: Knowledge Discovery in Databases: An Attribute-Oriented Approach. VLDB 1992: 547-559
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