Skip to main content
Springer Nature Link
Log in

Relational Data Mining

  • Chapter
  • First Online:
Data Mining and Knowledge Discovery Handbook

Summary

Data Mining algorithms look for patterns in data. While most existing Data Mining approaches look for patterns in a single data table, relational Data Mining (RDM) approaches look for patterns that involve multiple tables (relations) from a relational database. In recent years, the most common types of patterns and approaches considered in Data Mining have been extended to the relational case and RDM now encompasses relational association rule discovery and relational decision tree induction, among others. RDM approaches have been successfully applied to a number of problems in a variety of areas, most notably in the area of bioinformatics. This chapter provides a brief introduction to RDM.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  • Agrawal R. and Srikant R. , Mining sequential patterns. In Proceedings of the Eleventh International Conference on Data Engineering, pages 3–14. IEEE Computer Society Press, Los Alamitos, CA, 1995.

    Google Scholar 

  • Agrawal R., Mannila H., Srikant R., Toivonen H., and Verkamo A. I., Fast discovery of association rules. In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 307–328. AAAI Press, Menlo Park, CA, 1996.

    Google Scholar 

  • Blockeel H. and De Raedt L., Top-down induction of first order logical decision trees. Artificial Intelligence, 101: 285–297, 1998.

    Article  MATH  MathSciNet  Google Scholar 

  • Bratko I., Prolog Programming for Artificial Intelligence, 3rd edition. Addison Wesley, Harlow, England, 2001.

    Google Scholar 

  • Breiman L., Friedman J. H., Olshen R. A., and Stone C. J., Classification and Regression Trees. Wadsworth, Belmont, 1984.

    MATH  Google Scholar 

  • Clark P. and Boswel, R., Rule induction with CN2: Some recent improvements. In Proceedings of the Fifth European Working Session on Learning, pages 151–163. Springer, Berlin, 1991.

    Google Scholar 

  • Clark P. and Niblett T., The CN2 induction algorithm. Machine Learning, 3(4): 261–283, 1989.

    Google Scholar 

  • Dehaspe L., Toivonen H., and King R. D., Finding frequent substructures in chemical compounds. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pages 30–36. AAAI Press, Menlo Park, CA, 1998.

    Google Scholar 

  • Dehaspe L. and Toivonen H., Discovery of frequent datalog patterns. Data Mining and Knowledge Discovery, 3(1): 7–36, 1999.

    Article  Google Scholar 

  • Dehaspe L. and Toivonen H., Discovery of Relational Association Rules. In (Džeroski and Lavrač, 2001), pages 189–212, 2001.

    Google Scholar 

  • De Raedt L., editor. Advances in Inductive Logic Programming. IOS Press, Amsterdam, 1996.

    MATH  Google Scholar 

  • De Raedt L., Attribute-value learning versus inductive logic programming: the missing links (extended abstract). In Proceedings of the Eighth International Conference on Inductive Logic Programming, pages 1–8. Springer, Berlin, 1998.

    Google Scholar 

  • De Raedt L., Blockeel H., Dehaspe L., and Van LaerW., Three Companions for Data Mining in First Order Logic. In (Džeroski and Lavrač, 2001), pages 105–139, 2001.

    Google Scholar 

  • De Raedt L. and Džeroski S., First order jk-clausal theories are PAC-learnable. Artificial Intelligence, 70: 375–392, 1994.

    Article  MATH  MathSciNet  Google Scholar 

  • Džeroski S. and Lavrač N., editors. Relational Data Mining. Springer, Berlin, 2001.

    MATH  Google Scholar 

  • Džeroski S., Muggleton S., and Russell S., PAC-learnability of determinate logic programs. In Proceedings of the Fifth ACM Workshop on Computational Learning Theory, pages 128–135. ACM Press, New York, 1992.

    Google Scholar 

  • Džeroski S., Schulze-Kremer S., Heidtke K., Siems K., Wettschereck D., and Blockeel H., Diterpene structure elucidation from 13C NMR spectra with Inductive Logic Programming. Applied Artificial Intelligence, 12: 363–383, 1998.

    Article  Google Scholar 

  • Džeroski S., Blockeel H., Kompare B., Kramer S., Pfahringer B., and Van Laer W., Experiments in Predicting Biodegradability. In Proceedings of the Ninth International Workshop on Inductive Logic Programming, pages 80–91. Springer, Berlin, 1999.

    Google Scholar 

  • Džeroski S., Relational Data Mining Applications: An Overview. In (Džeroski and Lavrač, 2001), pages 339–364, 2001.

    Google Scholar 

  • Džeroski S., De Raedt L., and Wrobel S., editors. Proceedings of the First International Workshop on Multi-Relational Data Mining. KDD-2002: Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Canada, 2002.

    Google Scholar 

  • Emde W. and Wettschereck D., Relational instance-based learning. In Proceedings of the Thirteenth International Conference on Machine Learning, pages 122–130. Morgan Kaufmann, San Mateo, CA, 1996.

    Google Scholar 

  • King R.D., Karwath A., Clare A., and Dehaspe L., Genome scale prediction of protein functional class from sequence using Data Mining. In Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining, pages 384–389. ACM Press, New York, 2000.

    Google Scholar 

  • Kirsten M., Wrobel S., and Horváth T., Distance Based Approaches to Relational Learning and Clustering. In (Džeroski and Lavrač, 2001), pages 213–232, 2001.

    Google Scholar 

  • Kramer S., Structural regression trees. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pages 812–819. MIT Press, Cambridge, MA, 1996.

    Google Scholar 

  • Kramer S. and Widmer G., Inducing Classification and Regression Trees in First Order Logic. In (Džeroski and Lavrač, 2001), pages 140–159, 2001.

    Google Scholar 

  • Kramer S., Lavrač N., and Flach P., Propositionalization Approaches to Relational Data Mining. In (Džeroski and Lavrač, 2001), pages 262–291, 2001.

    Google Scholar 

  • Lavrač N., Džeroski S., and Grobelnik M., Learning nonrecursive definitions of relations with LINUS. In Proceedings of the Fifth EuropeanWorking Session on Learning, pages 265–281. Springer, Berlin, 1991.

    Google Scholar 

  • Lavrač N. and Džeroski S., Inductive Logic Programming: Techniques and Applications. Ellis Horwood, Chichester, 1994.

    Google Scholar 

  • Lloyd J., Foundations of Logic Programming, 2nd edition. Springer, Berlin, 1987.

    MATH  Google Scholar 

  • Mannila H. and Toivonen H., Discovering generalized episodes using minimal occurrences. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pages 146–151. AAAI Press, Menlo Park, CA, 1996.

    Google Scholar 

  • Michalski R., Mozetič I., Hong J., and Lavrač N., The multi-purpose incremental learning system AQ15 and its testing application on three medical domains. In Proceedings of the Fifth National Conference on Artificial Intelligence, pages 1041–1045. Morgan Kaufmann, San Mateo, CA, 1986.

    Google Scholar 

  • Muggleton S., Inductive logic programming. New Generation Computing, 8 (4) : 295–318, 1991.

    Article  MATH  Google Scholar 

  • Muggleton S., editor. Inductive Logic Programming. Academic Press, London, 1992.

    MATH  Google Scholar 

  • Muggleton S., Inverse entailment and Progol. New Generation Computing, 13: 245–286, 1995.

    Article  Google Scholar 

  • Muggleton S. and Feng C., Efficient induction of logic programs. In Proceedings of the First Conference on Algorithmic Learning Theory, pages 368–381. Ohmsha, Tokyo, 1990.

    Google Scholar 

  • Nedellec C., Rouveirol C., Ade H., Bergadano F., and Tausend B., Declarative bias in inductive logic programming. In L. De Raedt, editor, Advances in Inductive Logic Programming, pages 82–103. IOS Press, Amsterdam, 1996.

    Google Scholar 

  • Nienhuys-Cheng S.-H. and de Wolf R., Foundations of Inductive Logic Programming. Springer, Berlin, 1997.

    Google Scholar 

  • Plotkin G., A note on inductive generalization. In B. Meltzer and D. Michie, editors, Machine Intelligence 5, pages 153–163. Edinburgh Univ. Press, 1969.

    Google Scholar 

  • Quinlan J. R., Learning logical definitions from relations. Machine Learning, 5(3): 239–266, 1990.

    Google Scholar 

  • Quinlan J. R., C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.

    Google Scholar 

  • Rokach, L., Averbuch, M., and Maimon, O., Information retrieval system for medical narrative reports (pp. 217228). Lecture notes in artificial intelligence, 3055. Springer-Verlag (2004).

    Google Scholar 

  • Rokach L. and Maimon O., Data mining for improving the quality of manufacturing: A feature set decomposition approach. Journal of Intelligent Manufacturing 17(3): 285299, 2006.

    Article  Google Scholar 

  • Shapiro E., Algorithmic Program Debugging. MIT Press, Cambridge, MA, 1983.

    Google Scholar 

  • Srikant R. and Agrawal R., Mining generalized association rules. In Proceedings of the Twenty-first International Conference on Very Large Data Bases, pages 407–419. Morgan Kaufmann, San Mateo, CA, 1995.

    Google Scholar 

  • Ullman J., Principles of Database and Knowledge Base Systems, volume 1. Computer Science Press, Rockville, MA, 1988.

    Google Scholar 

  • Van Laer V. and De Raedt L., How to Upgrade Propositional Learners to First Order Logic: A Case Study. In (Džeroski and Lavrač, 2001), pages 235–261, 2001.

    Google Scholar 

  • Wrobel S., Inductive Logic Programming for Knowledge Discovery in Databases. In (Džeroski and Lavrač, 2001), pages 74–101, 2001.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Jožef Stefan Institute, Jamova 39, SI-1000, Ljubljana, Slovenia

    Sašo Džeroski

Authors
  1. Sašo Džeroski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sašo Džeroski .

Editor information

Editors and Affiliations

  1. , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Oded Maimon

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Džeroski, S. (2009). Relational Data Mining. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_46

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-09823-4_46

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-09822-7

  • Online ISBN: 978-0-387-09823-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics