DBMiner: a system for data mining in relational databases and data warehouses

ABSTRACT

A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses. The system implements a wide spectrum of data mining functions, including characterization, comparison, association, classification, prediction, and clustering. By incorporating several interesting data mining techniques, including OLAP and attribute-oriented induction, statistical analysis, progressive deepening for mining multiple-level knowledge, and meta-rule guided mining, the system provides a user-friendly, interactive data mining environment with good performance.

REFERENCES

• 1: Sameet Agarwal , Rakesh Agrawal , Prasad Deshpande , Ashish Gupta , Jeffrey F. Naughton , Raghu Ramakrishnan , Sunita Sarawagi, On the Computation of Multidimensional Aggregates, Proceedings of the 22th International Conference on Very Large Data Bases, p.506-521, September 03-06, 1996
• 2: Rakesh Agrawal , Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, p.487-499, September 12-15, 1994
• 3: Surajit Chaudhuri , Umeshwar Dayal, An overview of data warehousing and OLAP technology, ACM SIGMOD Record, v.26 n.1, p.65-74, March 1997 [doi>10.1145/248603.248616]
• 4: Usama M. Fayyad , Gregory Piatetsky-Shapiro , Padhraic Smyth , Ramasamy Uthurusamy, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, Menlo Park, CA, 1996
• 5: {5} Y. Fu and J. Han. Meta-rule-guided mining of association rules in relational databases. In Proc. 1st Int'l Workshop on Integration of Knowledge Discovery with Deductive and Object-Oriented Databases (KDOOD'95), pages 39-46, Singapore, Dec. 1995.
• 6: J. Han , Y. Cai , N. Cercone, Data-Driven Discovery of Quantitative Rules in Relational Databases, IEEE Transactions on Knowledge and Data Engineering, v.5 n.1, p.29-40, February 1993 [doi>10.1109/69.204089]
• 7: {7} J. Han and Y. Fu. Dynamic generation and refinement of concept hierarchies for knowledge discovery in databases. In Proc. AAAI'94 Workshop on Knowledge Discovery in Databases (KDD'94), pages 157-168, Seattle, WA, July 1994.
• 8: Jiawei Han , Yongjian Fu, Discovery of Multiple-Level Association Rules from Large Databases, Proceedings of the 21th International Conference on Very Large Data Bases, p.420-431, September 11-15, 1995
• 9: Jiawei Han , Yongjian Fu, Attribute-oriented induction in data mining, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, Menlo Park, CA, 1996
• 10: {10} J. Han, Y. Fu, W. Wang, J. Chiang, W. Gong, K. Koperski, D. Li, Y. Lu, A. Rajan, N. Stefanovic, B. Xia, and O. R. Zaiane. DBMiner: A system for mining knowledge in large relational databases. In Proc. 1996 Int'l Conf. on Data Mining and Knowledge Discovery (KDD'96), pages 250-255, Portland, Oregon, August 1996.
• 11: {11} M. Kamber, J. Han, and J. Y. Chiang. Metarule-guided mining of multi-dimensional association rules using data cubes. In Proc. 3rd Int. Conf. on Knowledge Discovery and Data Mining (KDD'96), pages 207-210, Newport Beach, California, August 1997.
• 12: M. Kamber , L. Winstone , Wan Gong , Shan Cheng , Jiawei Han, Generalization and decision tree induction: efficient classification in data mining, Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications, p.111, April 07-08, 1997
• 13: Manish Mehta , Rakesh Agrawal , Jorma Rissanen, SLIQ: A Fast Scalable Classifier for Data Mining, Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology, p.18-32, March 25-29, 1996
• 14: Gregory Piateski , William Frawley, Knowledge Discovery in Databases, MIT Press, Cambridge, MA, 1991
• 15: J. Ross Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993
• 16: Wei-Min Shen , KayLiang Ong , Bharat Mitbander , Carlo Zaniolo, Metaqueries for data mining, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, Menlo Park, CA, 1996
• 17: Ramakrishnan Srikant , Rakesh Agrawal, Mining Generalized Association Rules, Proceedings of the 21th International Conference on Very Large Data Bases, p.407-419, September 11-15, 1995
• 18: Yihong Zhao , Prasad M. Deshpande , Jeffrey F. Naughton, An array-based algorithm for simultaneous multidimensional aggregates, Proceedings of the 1997 ACM SIGMOD international conference on Management of data, p.159-170, May 11-15, 1997, Tucson, Arizona, United States

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Time series analysis

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Induction; Knowledge acquisition
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
      I.5.5 Implementation
          Subjects: Interactive systems

General Terms:
Algorithms, Design, Experimentation, Performance, Theory