ACM SIGMOD Anthology VLDB dblp.uni-trier.de

Knowledge Discovery in Databases: An Attribute-Oriented Approach.

Jiawei Han, Yandong Cai, Nick Cercone: Knowledge Discovery in Databases: An Attribute-Oriented Approach. VLDB 1992: 547-559
@inproceedings{DBLP:conf/vldb/HanCC92,
  author    = {Jiawei Han and
               Yandong Cai and
               Nick Cercone},
  editor    = {Li-Yan Yuan},
  title     = {Knowledge Discovery in Databases: An Attribute-Oriented Approach},
  booktitle = {18th International Conference on Very Large Data Bases, August
               23-27, 1992, Vancouver, Canada, Proceedings},
  publisher = {Morgan Kaufmann},
  year      = {1992},
  isbn      = {1-55860-151-1},
  pages     = {547-559},
  ee        = {db/conf/vldb/HanCC92.html},
  crossref  = {DBLP:conf/vldb/92},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

Knowledge discovery in databases, or data mining, is an important issue in the development of data- and knowledge-base systems. An attribute-oriented induction method has been developed for knowledge discovery in databases. The method integrates a machine learning paradigm, especially learning-from-examples techniques, with set-oriented database operations and extracts generalized data from actual data in databases. An attribute-oriented concept tree ascension technique is applied in generalization, which substantially reduces the computational complexity of database learning processes. Different kinds of knowledge rules, including characteristic rules, discrimination rules, quantitative rules, and data evolution regularities can be discovered efficiently using the attribute-oriented approach. In addition to learning in relational databases, the approach can be applied toknowledge discovery in nested relational and deductive databases. Learning can also be performed with databases containing noisy data and exceptional cases using database statistics. Furthermore, the rules discovered can be used to query database knowledge, answer cooperative queries and facilitate semantic query optimization. Based upon these principles, a prototyped database learning system, DBLEARN, has been constructed for experimentation.

Copyright © 1992 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

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Li-Yan Yuan (Ed.): 18th International Conference on Very Large Data Bases, August 23-27, 1992, Vancouver, Canada, Proceedings. Morgan Kaufmann 1992, ISBN 1-55860-151-1
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Referenced by

  1. Sunil Choenni: Design and Implementation of a Genetic-Based Algorithm for Data Mining. VLDB 2000: 33-42
  2. Wen-Chi Hou: A Framework for Statistical Data Mining with Summary Tables. SSDBM 1999: 14-23
  3. San-Yih Hwang, Sun-Fa Ho, Jian Tang: Mining Exception Instances to Facilitate Workflow Exception Handling. DASFAA 1999: 45-52
  4. Ming-Syan Chen, Jong Soo Park, Philip S. Yu: Efficient Data Mining for Path Traversal Patterns. IEEE Trans. Knowl. Data Eng. 10(2): 209-221(1998)
  5. Edwin M. Knorr, Raymond T. Ng: Algorithms for Mining Distance-Based Outliers in Large Datasets. VLDB 1998: 392-403
  6. Noureddine Mouaddib, Guillaume Raschia: A Fuzzy Attribute-Oriented Induction Method for Knowledge Discovery in Relational Databases. ER Workshops 1998: 1-13
  7. 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
  8. Sunil Choenni: On the Suitability of Genetic-Based Algorithms for Data Mining. ER Workshops 1998: 55-67
  9. Tamas Abraham, John F. Roddick: Incremental Meta-Mining from Large Temporal Data Sets. ER Workshops 1998: 41-54
  10. Laks V. S. Lakshmanan, Nicola Leone, Robert B. Ross, V. S. Subrahmanian: ProbView: A Flexible Probabilistic Database System. ACM Trans. Database Syst. 22(3): 419-469(1997)
  11. Jong Soo Park, Ming-Syan Chen, Philip S. Yu: Using a Hash-Based Method with Transaction Trimming for Mining Association Rules. IEEE Trans. Knowl. Data Eng. 9(5): 813-825(1997)
  12. Raymond T. Ng: Semantics, Consistency, and Query Processing of Empirical Deductive Databases. IEEE Trans. Knowl. Data Eng. 9(1): 32-49(1997)
  13. Edwin M. Knorr, Raymond T. Ng: Finding Aggregate Proximity Relationships and Commonalities in Spatial Data Mining. IEEE Trans. Knowl. Data Eng. 8(6): 884-897(1996)
  14. Wen-Chi Hou: Extraction and Applications of Statistical Relationships in Relational Databases. IEEE Trans. Knowl. Data Eng. 8(6): 939-945(1996)
  15. 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)
  16. Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, Takeshi Tokuyama: Data Mining Using Two-Dimensional Optimized Accociation Rules: Scheme, Algorithms, and Visualization. SIGMOD Conference 1996: 13-23
  17. Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, Takeshi Tokuyama: Mining Optimized Association Rules for Numeric Attributes. PODS 1996: 182-191
  18. Xiaohua Hu, Nick Cercone: Mining Knowledge Rules from Databases: A Rough Set Approach. ICDE 1996: 96-105
  19. I-Min A. Chen: Query Answering Using Discovered Rules. ICDE 1996: 402-411
  20. Ashok Savasere, Edward Omiecinski, Shamkant B. Navathe: An Efficient Algorithm for Mining Association Rules in Large Databases. VLDB 1995: 432-444
  21. Hongjun Lu, Rudy Setiono, Huan Liu: NeuroRule: A Connectionist Approach to Data Mining. VLDB 1995: 478-489
  22. Jong Soo Park, Ming-Syan Chen, Philip S. Yu: An Effective Hash Based Algorithm for Mining Association Rules. SIGMOD Conference 1995: 175-186
  23. Maurice A. W. Houtsma, Arun N. Swami: Set-Oriented Mining for Association Rules in Relational Databases. ICDE 1995: 25-33
  24. Tadashi Ohmori, Mamoru Hoshi: Gaming-Simulations of Multi-Agent Information Systems using Large Databases: The Concept and Database Algorithms. DASFAA 1995: 95-106
  25. Jong Soo Park, Ming-Syan Chen, Philip S. Yu: Efficient Parallel and Data Mining for Association Rules. CIKM 1995: 31-36
  26. Raymond T. Ng, Jiawei Han: Efficient and Effective Clustering Methods for Spatial Data Mining. VLDB 1994: 144-155
  27. Rakesh Agrawal, Ramakrishnan Srikant: Fast Algorithms for Mining Association Rules in Large Databases. VLDB 1994: 487-499
  28. Jason Tsong-Li Wang, Gung-Wei Chirn, Thomas G. Marr, Bruce A. Shapiro, Dennis Shasha, Kaizhong Zhang: Combinatorial Pattern Discovery for Scientific Data: Some Preliminary Results. SIGMOD Conference 1994: 115-125
  29. Jiawei Han, Yongjian Fu, Yue Huang, Yandong Cai, Nick Cercone: DBLearn: A System Prototype for Knowledge Discovery in Relational Databases. SIGMOD Conference 1994: 516
  30. Rakesh Agrawal: Tutorial Database Mining. PODS 1994: 75-76
  31. Doheon Lee, Myoung-Ho Kim: Discovering Database Summaries through Refinements of Fuzzy Hypotheses. ICDE 1994: 223-230
  32. Vasant Dhar, Alexander Tuzhilin: Abstract-Driven Pattern Discovery in Databases. IEEE Trans. Knowl. Data Eng. 5(6): 926-938(1993)
  33. David A. Bell: From Data Properties to Evidence. IEEE Trans. Knowl. Data Eng. 5(6): 965-969(1993)
  34. Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Database Mining: A Performance Perspective. IEEE Trans. Knowl. Data Eng. 5(6): 914-925(1993)
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