Skip to main content
Springer Nature Link
Log in

An Integrated Multi-task Inductive Database VINLEN: Initial Implementation and Early Results

  • Conference paper
Knowledge Discovery in Inductive Databases (KDID 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4747))

Included in the following conference series:

Abstract

A brief review of the current research on the development of the VINLEN multitask inductive database and decision support system is presented. The aim of this research is to integrate a wide range of knowledge generation operators in one system that given input data and relevant domain knowledge generates new knowledge according to the user’s goal. The central VINLEN operator is natural induction that generates hypotheses from data in the form of attributional rules that resemble natural language expressions, and are easy to understand and interpret. This operator is illustrated by an application to discovering relationships between lifestyles and diseases of men age 50-65 in a large database created by the American Medical Association. The conclusion outlines plans for future research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Blockeel, H.: Experiment Databases: A Novel Methodology for Experimental Research. In: Bonchi, F., Boulicaut, J.-F. (eds.) KDID 2005. LNCS, vol. 3933, pp. 72–85. Springer, Heidelberg (2006)

    Google Scholar 

  2. De Raedt, L.: A Perspective on Inductive Databases. ACM SIGKDD Explorations Newsletter 4(2), 69–77 (2002)

    Article  Google Scholar 

  3. Finin, T., Fritzson, R., McKay, D., McEntire, R.: KQML as an Agent Communication Language. In: Proceedings of the Third International Conference on Information and Knowledge Management, CIKM 1994, pp. 456–463. ACM Press, New York (1994)

    Chapter  Google Scholar 

  4. Flach, P., Dzeroski, S.: Editorial: Inductive Logic Programming is Coming of Age. Machine Learning 44(3), 207–209 (2001)

    Article  MATH  Google Scholar 

  5. Głowiński, C., Michalski, R.S.: Discovering Multi-head Attributional Rules in Large Databases. In: Tenth International Symposium on Intelligent Information Systems, Zakopane, Poland (2001)

    Google Scholar 

  6. Han, J., Kamber, M.: Data Mining – Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  7. Hätönen, K., Mika Klemettinen, M., Miettinen, M.: Remarks on the Industrial Application of Inductive Database Technologies. In: Boulicaut, J.-F., De Raedt, L., Mannila, H. (eds.) Constraint-Based Mining and Inductive Databases. LNCS (LNAI), vol. 3848, pp. 196–215. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  8. Imieliński, T., Virmani, A.: MSQL: A query language for database mining. Data Mining and Knowledge Discovery 3(4), 373–408 (1999)

    Article  Google Scholar 

  9. Imieliński, T., Mannila, H.: A Database Perspective on Knowledge Discovery. Communications of the ACM 39, 58–64 (1996)

    Article  Google Scholar 

  10. Kaufman, K., Michalski, R.S.: A Knowledge Scout for Discovering Medical Patterns: Methodology and System SCAMP. In: Proceedings of the Fourth International Conference on Flexible Query Answering Systems, FQAS’2000, Warsaw, Poland, pp. 485–496 (2000)

    Google Scholar 

  11. Meo, R., Giuseppe, P., Stefano, C.: An Extension to SQL for Mining Association Rules. Data Mining and Knowledge Discovery V2(2), 195–224 (1998)

    Article  Google Scholar 

  12. Meo, R., Lanzi, P.L., Klemettinen, M. (eds.): Database Support for Data Mining Applications. LNCS (LNAI), vol. 2682. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  13. Meo, R., Vernier, F., Barreri, R., Matera, M., Carregio, D.: Applying a Data Mining Query Language to the Discovery of Interesting Patterns in WEB Logs. In: Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany (2004)

    Google Scholar 

  14. Michalski, R.S.: ATTRIBUTIONAL CALCULUS: A Logic and Representation Language for Natural Induction. Reports of the Machine Learning and Inference Laboratory, MLI 04-2, George Mason University, Fairfax, VA (2004)

    Google Scholar 

  15. Michalski, R.S.: Attributional Ruletrees: A New Representation for AQ Learning. Reports of the Machine Learning and Inference Laboratory, MLI 02-1, George Mason University, Fairfax, VA (October 2002) (slightly updated in May 2004)

    Google Scholar 

  16. Michalski, R.S., Kerschberg, L., Kaufman, K., Ribeiro, J.: Mining For Knowledge in Databases: The INLEN Architecture, Initial Implementation and First Results. Intelligent Information Systems: Integrating Artificial Intelligence and Database Technologies 1(1), 85–113 (1992)

    Google Scholar 

  17. Michalski, R.S., Wojtusiak, J.: Reasoning with Meta-values in AQ Learning. Reports of the Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA (2006)

    Google Scholar 

  18. Seeman, W.D., Michalski, R.S.: The CLUSTER3 System for Goal-oriented Conceptual Clustering: Method and Preliminary Results. In: Proceedings of the Data Mining and Information Engineering Conference, Prague, Czech Republic (2006)

    Google Scholar 

  19. Śnieżyński, B., Szymacha, R., Michalski, R.S.: Knowledge Visualization Using Optimized General Logic Diagrams. In: Proceedings of the Intelligent Information Processing and Web Mining Conference, Gdansk, Poland (2005)

    Google Scholar 

  20. Szydło, T., Śnieżyński, B., Michalski, R.S.: A Rules-to-Trees Conversion in the Inductive Database System VINLEN. In: Proceedings of the Intelligent Information Processing and Web Mining Conference, Gdansk, Poland (2005)

    Google Scholar 

  21. Wojtusiak, J.: AQ21 User’s Guide. Reports of the Machine Learning and Inference Laboratory, MLI 04-3, George Mason University, Fairfax, VA (2004) (updated in September 2005)

    Google Scholar 

  22. Wojtusiak, J., Michalski, R.S.: The LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function Optimization Problems. In: Proceedings of Genetic and Evolutionary Computation Conference, Seattle, WA (2006)

    Google Scholar 

  23. Wojtusiak, J., Michalski, R.S., Kaufman, K., Pietrzykowski, J.: The AQ21 Natural Induction Program for Pattern Discovery: Initial Version and its Novel Features. In: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence, Washington D.C., IEEE Computer Society Press, Los Alamitos (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Machine Learning and Inference Laboratory, George Mason University,  

    Kenneth A. Kaufman, Ryszard S. Michalski, Jarosław Pietrzykowski & Janusz Wojtusiak

Authors
  1. Kenneth A. Kaufman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ryszard S. Michalski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jarosław Pietrzykowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Janusz Wojtusiak
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Sašo Džeroski Jan Struyf

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kaufman, K.A., Michalski, R.S., Pietrzykowski, J., Wojtusiak, J. (2007). An Integrated Multi-task Inductive Database VINLEN: Initial Implementation and Early Results. In: Džeroski, S., Struyf, J. (eds) Knowledge Discovery in Inductive Databases. KDID 2006. Lecture Notes in Computer Science, vol 4747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75549-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-75549-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75548-7

  • Online ISBN: 978-3-540-75549-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics