Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

In a vertical representation of a market-basket database, each item is associated with a column of values representing the transactions in which it is present. The association-rule mining algorithms that have been recently proposed for this representation show performance improvements over their classical horizontal counterparts, but are either efficient only for certain database sizes, or assume particular characteristics of the database contents, or are applicable only to specific kinds of database schemas. We present here a new vertical mining algorithm called VIPER, which is general-purpose, making no special requirements of the underlying database. VIPER stores data in compressed bit-vectors called “snakes” and integrates a number of novel optimizations for efficient snake generation, intersection, counting and storage. We analyze the performance of VIPER for a range of synthetic database workloads. Our experimental results indicate significant performance gains, especially for large databases, over previously proposed vertical and horizontal mining algorithms. In fact, there are even workload regions where VIPER outperforms an optimal, but practically infeasible, horizontal mining algorithm.

REFERENCES

• 1: Rakesh Agrawal , Tomasz Imieliński , Arun Swami, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, p.207-216, May 25-28, 1993, Washington, D.C., United States
• 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: Data Organization and Access for Efficient Data Mining, Proceedings of the 15th International Conference on Data Engineering, p.522, March 23-26, 1999
• 4: G. Gardarin, P. Pucheral, and F. Wu. Bitmap based algorithms for mining association rules. Technical report 1998-18, University of Versailles, 1998. (http://www.prism.uvsq.fr/rapports/1998/ document_1998_18.ps.gz)
• 5: S.W. Golomb. Run-length encoding. IEEE Trans. on Information Theory, 12(3), 3uly 1966.
• 6: M. Holsheimer, M. Kersten, H. Mannila, and H. Toivonen. A perspective on databases and data mining. In Proc. of 1st Intl. Conf. on Knowledge Discovery and Data Mining (KDD), August 1995.
• 7: Ashoka Savasere , Edward Omiecinski , Shamkant B. Navathe, An Efficient Algorithm for Mining Association Rules in Large Databases, Proceedings of the 21th International Conference on Very Large Data Bases, p.432-444, September 11-15, 1995
• 8: P. Shenoy, 3. Haritsa, S. Sudarshan, M. Bawa, G. Bhalotia, and D. Shah. Turbo-charging vertical mining of large databases. Technical Report TR-2000-02, DSL, Indian Institute of Science, 2000. (http://dsl.serc.iisc.ernet.in/pub/TR/TR-2000-02.ps)
• 9: Show-Jane Yen , Arbee L. P. Chen, An efficient approach to discovering knowledge from large databases, Proceedings of the fourth international conference on on Parallel and distributed information systems, p.8-18, December 18-20, 1996, Miami Beach, Florida, United States
• 10: Mohammed Javeed Zaki , Wei Li, Scalable data mining for rules, 1998
• 11: M. J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li. New algorithms for fast discovery of association rules. In Proc. of 3rd Intl. Conf. on Knowledge Discovery and Data Mining (KDD), August 1997.

INDEX TERMS

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

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT

General Terms:
Design, Management, Performance