1995 KnowledgeMiningInDatabases

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Subject Headings: Data Mining, DBMiner

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Abstract

Active research has been conducted on knowledge discovery in databases by the researchers in our group for years, with many interesting results published and a prototyped knowledge discovery system, DBMiner (previously called DBLearn), developed and demonstrated in several conferences. Our research covers a wide spectrum of knowledge discovery, including (1) the study of knowledge discovery in relational, object-oriented, deductive, spatial, and active databases, and global information systems, and (2) the development of various kinds of knowledge discovery methods, including attribute-oriented induction, progressive deepening for mining multiple-level rules, meta-rule guided knowledge mining, etc. Techniques for the discovery of various kinds of knowledge, including generalization, characterization, discrimination, association, classification, clustering, etc. and the application of knowledge discovery for intelligent query answering, multiple-layered database construction, etc. have also been studied in our research.

References

  • Usama M. Fayyad, G. Piatetsky-Shapiro, Padhraic Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1995.
  • Y. Fu and Jiawei Han. Meta-rule-guided mining of association rules in relational databases. In: Proceedings of 1st Int'l Workshop on Integration of Knowledge Discovery with Deductive and Object-Oriented Databases (KDOOD'95), Singapore, Dec. 1995.
  • Jiawei Han, Y. Cai, and N. Cercone. Knowledge discovery in databases: An attribute-oriented approach. In: Proceedings of 18th International Conference Very Large Data Bases, pages 547{559, Vancouver, Canada, August 1992.
  • Jiawei Han, Y. Cai, and N. Cercone. Data-driven discovery of quantitative rules in relational databases. IEEE Trans. Knowledge and Data Engineering, 5:29{40, 1993.
  • Jiawei Han and Y. Fu. Dynamic generation and re nement of concept hierarchies for knowledge discovery in databases. In: Proceedings. AAAI'94 Workshop on Knowledge Discovery in Databases (KDD'94), pages 157{168, Seattle, WA, July 1994.
  • Jiawei Han and Y. Fu. Discovery of multiple-level association rules from large databases. In: Proceedings of 1995 International Conference Very Large Data Bases, Zurich, Switzerland, Sept. 1995.
  • Jiawei Han and Y. Fu. Exploration of the power of attribute-oriented induction in data mining. In U.M. Fayyad, G. Piatetsky-Shapiro, Padhraic Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1995.
  • Jiawei Han, Y. Fu, Y. Huang, Y. Cai, and N. Cercone. DBLearn: A system prototype for knowledge discovery in relational databases. In: Proceedings of 1994 ACM-SIGMOD Conference Management of Data, page 516, Minneapolis, MN, May 1994.
  • Jiawei Han, Y. Fu, and R. Ng. Cooperative query answering using multiple-layered databases. In: Proceedings of 2nd International Conference Cooperative Information Systems, pages 47{58, Toronto, Canada, May 1994.
  • Jiawei Han, Y. Fu, and S. Tang. Advances of the DBLearn system for knowledge discovery in large databases. In: Proceedings of 1995 Int'l Joint Conference on Artificial Intelligence, Montreal, Canada, Aug. 1995.
  • Jiawei Han, Y. Huang, N. Cercone, and Y. Fu. Intelligent query answering by knowledge discovery techniques. In IEEE Trans. Knowledge and Data Engineering (accepted), 1995.
  • Jiawei Han, S. Nishio, and H. Kawano. Knowledge discovery in object-oriented and active databases. In F. Fuchi and T. Yokoi, editors, Knowledge Building and Knowledge Sharing, pages 221{230. Ohmsha, Ltd. and IOS Press, 1994.
  • K. Koperski and Jiawei Han. Discovery of spatial association rules in geographic information databases. In: Proceedings of 4th Int'l Symp. on Large Spatial Databases (SSD'95), Portland, Maine, Aug. 1995.
  • W. Lu, Jiawei Han, and B. C. Ooi. Knowledge discovery in large spatial databases. In Far East Workshop on Geographic Information Systems, pages 275{289, Singapore, June 1993.
  • R. S. Michalski. A theory and methodology of inductive learning. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, pages 83{134. Morgan Kaufmann, 1983.
  • R. S. Michalski and R. Stepp. Automated construction of classifications: Conceptual clustering versus numerical taxonomy. IEEE Trans. Pattern Analysis and Machine Intelligence]], 5:396{410, 1983.
  • R. Ng and Jiawei Han. E cient and effective clustering method for spatial data mining. In: Proceedings of 1994 International Conference Very Large Data Bases, pages 144{155, Santiago, Chile, September 1994.
  • G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991.
  • J. Ross Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1992.
  • Osmar R. Zaiane, and Jiawei Han. (1995). “Resource and Knowledge Discovery in Global Information Systems: A preliminary design and experiment.” In: Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining (KDD 1995).

About the authors

  • Jiawei Han. Professor, Computing Science, Simon Fraser University. His major research interests include database and knowledge-base systems, knowledge discovery in databases, deductive and object-oriented databases, spatial and multi-media databases, logic programming, and artificial intelligence. He is the leader of the Canadian IRIS project IRIS:HMI-5 (\Data Mining and Knowledge Discovery in Large Databases"), and has served or is currently serving in the program committees of over 20 international conferences, including ICDE'95 (also, program committee vice-chairman), DOOD'95, ACM-SIGMOD'96, and VLDB'96.
  • Yongjian Fu. Ph.D. student, Computing Science, Simon Fraser University. He received M.Sc. and B.Sc. degrees at Zhejiang University, China, and worked as a lecturer there before joining SFU. He is the major developer of the DBMiner system and works on research into knowledge discovery from databases and applications of knowledge discovery systems.
  • Krzysztof Koperski. Ph.D. student, Computing Science, Simon Fraser University. His major research focus is on spatial data mining. He is also interested in spatial reasoning and spatial object-oriented databases.
  • Gabor Melli. M.Sc. student, Computing Science, Simon Fraser University. He is an expert on systems and has contributed to the system support of the relational database system engine. His current research focuses on automatic prediction of attribute-value behavior.
  • Wei Wang. M.Sc. student, Computing Science, Simon Fraser University. He has been implementing the GUI interface for the DBMiner system on PCs and is performing research on knowledge discovery in heterogeneous databases.
  • Osmar R. Zaiane. Ph.D. student, Computing Science, Simon Fraser University. He holds an M.Sc. degree in Computer Science from Laval University (Quebec, Canada) where he worked on mobile databases stored on smart-cards. He also holds an M.Sc. degree in Electronics from Paris XI University (Paris, France) where he worked on Natural Language Interfaces for textual databases. His current research focuses on knowledge discovery in global network information systems.,


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
1995 KnowledgeMiningInDatabasesJiawei Han
Yongjian Fu
Krzysztof Koperski
Gabor Melli
Wei Wang
Osmar R. Zaïane
Knowledge Mining in Databases: An Integration of Machine Learning Methodologies with Database TechnologiesCanadian AI Magazinehttp://www.cs.ualberta.ca/~zaiane/postscript/cai95.pdf1995