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Discovery of Relational Association Rules

  • Chapter
Relational Data Mining

Abstract

Within KDD, the discovery of frequent patterns has been studied in a variety of settings. In its simplest form, known from association rule mining, the task is to discover all frequent item sets, i.e., all combinations of items that are found in a sufficient number of examples. We present algorithms for relational association rule discovery that are well-suited for exploratory data mining. They offer the flexibility required to experiment with examples more complex than feature vectors and patterns more complex than item sets.

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References

  1. H. Adé, L. De Raedt, and M. Bruynooghe. Declarative bias for specific-togeneral ILP systems. Machine Learning, 20(1/2): 119–154, 1995.

    Article  Google Scholar 

  2. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 207–216. ACM Press, New York, 1993.

    Google Scholar 

  3. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. 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 

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

    Chapter  Google Scholar 

  5. H. Blockeel and L. De Raedt. Relational knowledge discovery in databases. In Proceedings of the Sixth International Workshop on Inductive Logic Programming, pages 199–212. Springer, Berlin, 1996.

    Google Scholar 

  6. H. Blockeel and L. De Raedt. Top-down induction of first order logical decision trees. Artificial Intelligence, 101(1–2): 285–297, 1998.

    Article  MathSciNet  MATH  Google Scholar 

  7. H. Blockeel, L. De Raedt, N. Jacobs, and B. Demoen. Scaling up inductive logic programming by learning from interpretations. Data Mining and Knowledge Discovery, 3(1): 59–93, 1999.

    Article  Google Scholar 

  8. I. Bratko. Prolog Programming for Artificial Intelligence, 2nd edition. Addison-Wesley, Wokingham, England, 1990.

    Google Scholar 

  9. L. Dehaspe. Frequent Pattern Discovery in First-Order Logic. PhD thesis. Department of Computer Science, Katholieke Universiteit Leuven, Belgium, 1998. Available at http://www.cs.kuleuven.ac.be/~ldh/.

    Google Scholar 

  10. L. Dehaspe and L. De Raedt. Mining association rules in multiple relations. In Proceedings of the Seventh International Workshop on Inductive Logic Programming, pages 125–132. Springer, Berlin, 1997.

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  12. L. Dehaspe, H. Toivonen, and R. D. King. 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 

  13. R. Elmasri and S. B. Navathe. Fundamentals of Database Systems, 2nd edition. Benjamin/Cummings, Redwood City, CA, 1989.

    MATH  Google Scholar 

  14. J. Han and Y. Fu. Discovery of multiple-level association rules from large databases. In Proceedings of the Twenty-first International Conference on Very Large Data Bases, pages 420–431. Morgan Kaufmann, San Mateo, CA, 1995.

    Google Scholar 

  15. M. Holsheimer, M. Kersten, H. Mannila, and H. Toivonen. A perspective on databases and data mining. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining, pages 150–155. AAAI Press, Menlo Park, CA, 1995.

    Google Scholar 

  16. W. Kloesgen. Problems for knowledge discovery in databases and their treatment in the statistics interpreter EXPLORA. International Journal of Intelligent Systems, 7(7): 649–673, 1992.

    Article  MATH  Google Scholar 

  17. W. Kloesgen. EXPLORA: A multipattern and multistrategy discovery assistant. In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 249–271. AAAI Press, Menlo Park, CA, 1996.

    Google Scholar 

  18. G. Lindner and K. Morik. Coupling a relational learning algorithm with a database system. In Proceedings of the MLnet Familiarization Workshop on Statistics, Machine Learning and Knowledge Discovery in Databases, FORTH, Heraklion, Greece, 1995.

    Google Scholar 

  19. H. Lu, R. Setiono, and H. Liu. Neurorule: A connectionist approach to data mining. In Proceedings of the Twenty-first International Conference on Very Large Data Bases, pages 478–489. Morgan Kaufmann, San Mateo, CA, 1995.

    Google Scholar 

  20. H. Mannila. Database methods for data mining. Tutorial notes, Fourth International Conference on Knowledge Discovery and Data Mining. Technical report, AAAI Press, Menlo Park, CA, 1998.

    Google Scholar 

  21. H. Mannila and H. Toivonen. 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 

  22. H. Mannila and H. Toivonen. Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery, 1(3): 241–258, 1997.

    Article  Google Scholar 

  23. H. Mannila, H. Toivonen, and A. I. Verkamo. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1(3): 259–289, 1997.

    Article  Google Scholar 

  24. T. Mitchell. Generalization as search. Artificial Intelligence, 18: 203–226, 1982.

    Article  MathSciNet  Google Scholar 

  25. S. Muggleton. Inverse entailment and Progol. New Generation Computing, 13, 1995.

    Google Scholar 

  26. S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19, 20: 629–679, 1994.

    Article  MathSciNet  Google Scholar 

  27. C. Nédellec, H. Adé, F. Bergadano, and B. Tausend. Declarative bias in ILP. In L. De Raedt, editor, Advances in Inductive Logic Programming, pages 82–103. IOS Press, Amsterdam, 1996.

    Google Scholar 

  28. G. Plotkin. A note on inductive generalization. In Machine Intelligence, pages 153–163. Edinburgh University Press, Edinburgh, 1970.

    Google Scholar 

  29. S. L. Salzberg. On comparing classifiers: pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery, 1(3): 317–328, 1997.

    Article  Google Scholar 

  30. A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In Proceedings of the Twenty-first International Conference on Very Large Data Bases, pages 432–444. Morgan Kaufmann, San Mateo, CA, 1995.

    Google Scholar 

  31. R. Srikant and R. Agrawal. 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 

  32. R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and performance improvements. In Proceedings of the Fifth International Conference on Extending Database Technology, pages 3–17. Springer, Berlin, 1996.

    Google Scholar 

  33. R. Srikant, Q. Vu, and R. Agrawal. Mining association rules with item constraints. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pages 67–73. AAAI Press, Menlo Park, CA, 1997.

    Google Scholar 

  34. L. Sterling and E. Shapiro. The art of Prolog. MIT Press, Cambridge, MA, 1986.

    MATH  Google Scholar 

  35. H. Toivonen. Sampling large databases for association rules. In Proceedings of the Twenty-second International Conference on Very Large Data Bases, pages 134–145. Morgan Kaufmann, San Mateo, CA, 1996.

    Google Scholar 

  36. I. Weber. Discovery of first-order regularities in a relational database using offline candidate determination. In Proceedings of the Seventh International Workshop on Inductive Logic Programming, pages 288–295. Springer, Berlin, 1997.

    Chapter  Google Scholar 

  37. I. Weber. A declarative language bias for levelwise search of first-order regularities. In Proceedings of Fachgruppentreffen Maschinelles Lernen. Technischer Bericht 98/11, Technische Universität, Berlin, 1998. http://www.informatik.uni-stuttgart.de/ifi/is/Personen/Irene/fgm198.ps.gz.

    Google Scholar 

  38. S. Wrobel. An algorithm for multi-relational discovery of subgroups. In Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery, pages 78–87. Springer, Berlin, 1997.

    Chapter  Google Scholar 

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Dehaspe, L., Toivonen, H. (2001). Discovery of Relational Association Rules. In: Džeroski, S., Lavrač, N. (eds) Relational Data Mining. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04599-2_8

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  • DOI: https://doi.org/10.1007/978-3-662-04599-2_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-07604-6

  • Online ISBN: 978-3-662-04599-2

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