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Inductive Databases and Constraint-based Data Mining: Introduction and Overview

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Inductive Databases and Constraint-Based Data Mining

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Abstract

We briefly introduce the notion of an inductive database, explain its relation to constraint-based data mining, and illustrate it on an example.We then discuss constraints and constraint-based data mining in more detail, followed by a discussion on knowledge discovery scenarios. We further give an overview of recent developments in the area, focussing on those made within the IQ project, that gave rise to most of the chapters included in this volume. We finally outline the structure of the book and summarize the chapters, following the structure of the book.

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References

  1. R. Agrawal, T. Imielinski, and A. Swami (1993). Mining association rules between sets of items in large databases. In Proc. ACM SIGMOD Conf. on Management of Data, pages 207–216. ACM Press, New York.

    Google Scholar 

  2. R. Bayardo, guest editor (2002). Constraints in data mining. Special issue of SIGKDD Explorations, 4(1).

    Google Scholar 

  3. C. Becquet, S. Blachon, B. Jeudy, J-F. Boulicaut, and O. Gandrillon (2002). Strongassociation-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data. Genome Biology, 3(12):research0067.

    Google Scholar 

  4. S. Bistarelli and F. Bonchi (2005). Interestingness is not a Dichotomy: Introducing Softness in Constrained Pattern Mining. In Proc. 9th European Conf. on Principles and Practice of Knowledge Discovery in Databases, pages 22–33. Springer, Berlin.

    Google Scholar 

  5. S. Blachon, R. G. Pensa, J. Besson, C. Robardet, J.-F. Boulicaut, and O. Gandrillon (2007). Clustering formal concepts to discover biologically relevant knowledge from gene expression data. In Silico Biology, 7(4–5): 467–483.

    Google Scholar 

  6. J-F. Boulicaut, A. Bykowski, C. Rigotti (2003). Free-sets: a condensed representation of boolean data for the approximation of frequency queries. Data Mining and Knowledge Discovery, 7(1):5–22.

    Article  MathSciNet  Google Scholar 

  7. J.-F. Boulicaut, L. De Raedt, and H. Mannila, editors (2005). Constraint-Based Mining and Inductive Databases. Springer, Berlin.

    MATH  Google Scholar 

  8. J-F. Boulicaut and B. Jeudy (2005). Constraint-based data mining. In O. Maimon and L. Rokach, editors, The Data Mining and Knowledge Discovery Handbook, pages 399–416. Springer, Berlin.

    Chapter  Google Scholar 

  9. J.-F. Boulicaut, M. Klemettinen, and H. Mannila (1999). Modeling KDD processes within the inductive database framework. In Proc. 1st Intl. Conf. on Data Warehousing and Knowledge Discovery, pages 293–302. Springer, Berlin.

    Google Scholar 

  10. B. Bringmann, A. Zimmermann, L. De Raedt, and S. Nijssen (2006) Don’t be af raid of simpler patterns. In Proc 10th European Conf. on Principles and Practice of Knowledge Discovery in Databases, pages 55–66. Springer, Berlin.

    Google Scholar 

  11. T. Calders, B. Goethals and A.B. Prado (2006a). Integrating pattern mining in relational databases. In Proc. 10th European Conf. on Principles and Practice of Knowledge Discovery in Databases, pages 454–461. Springer, Berlin.

    Google Scholar 

  12. T. Calders, L.V.S. Lakshmanan, R.T. Ng and J. Paredaens (2006b). Expressive power of an algebra for data mining. ACM Transactions on Database Systems, 31(4): 1169–1214.

    Article  Google Scholar 

  13. T. Calders, C. Rigotti and J.-F. Boulicaut (2005). A survey on condensed representations for frequent sets. In J.-F. Boulicaut, L. De Raedt, and H. Mannila, eds., Constraint-Based Mining and Inductive Databases, pages 64–80. Springer, Berlin.

    Google Scholar 

  14. L. Cerf, J. Besson, C. Robardet, and J-F. Boulicaut (2008). Data-Peeler: Constraint-based closed pattern mining in n-ary relations. In Proc. 8th SIAM Intl. Conf. on Data Mining, pages 37–48. SIAM, Philadelphia, PA.

    Google Scholar 

  15. L. De Raedt (2002a). A perspective on inductive databases. SIGKDD Explorations, 4(2): 69–77.

    Article  Google Scholar 

  16. L. De Raedt (2002b). Data mining as constraint logic programming. In A.C. Kakas and F. Sadri, editors, Computational Logic: Logic Programming and Beyond – Essays in Honour of Robert A. Kowalski, Part II, pages 113–125. Springer, Berlin.

    Google Scholar 

  17. L. De Raedt, T. Guns, and S. Nijssen (2008). Constraint programming for itemset mining. In Proc. 14th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, pages 204–212. ACM Press, New York.

    Google Scholar 

  18. S. Džeroski (2007). Towards a general framework for data mining. In 5th Intl. Wshp. on Knowledge Discovery in Inductive Databases: Revised Selected and Invited Papers, pages 259–300. Springer, Berlin.

    Google Scholar 

  19. U. Fayyad, G. Piatetsky-Shapiro and P. Smyth (1996). From data mining to knowledge discovery: An overview. In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 495–515. MIT Press, Cambridge, MA.

    Google Scholar 

  20. U. Fayyad, G. Piatetsky-Shapiro, and R. Uthurusamy (2003). Summary from the KDD-2003 panel – “Data Mining: The Next 10 Years”. SIGKDD Explorations, 5(2):191–196.

    Article  Google Scholar 

  21. G. C. Garriga, R. Khardon, and L. De Raedt (2007). On mining closed sets in multirelational data. In In Proc. 20th Intl. Joint Conf. on Artificial Intelligence, pages 804–809. AAAI Press, Menlo Park, CA.

    Google Scholar 

  22. A. Gionis, H. Mannila, T. Mielikainen, and P. Tsaparas (2006). Assessing data mining results via swap randomization. In Proc. 12th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, pages 167–176. ACM Press, New York.

    Google Scholar 

  23. N. Haiminen and H. Mannila (2007). Discovering isochores by least-squares optimal segmentation. Gene, 394(1–2):53–60.

    Article  Google Scholar 

  24. J. Han, L.V.S. Lakshmanan, R.T. Ng (1999). Constraint-Based Multidimensional Data Mining. IEEE Computer, 32(8):46–50.

    Google Scholar 

  25. D.J. Hand, H. Mannila, and P. Smyth (2001). Principles of Data Mining. MIT Press, Cambridge, MA.

    Google Scholar 

  26. T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Communications of the ACM, 39(11):58–64, 1996.

    Article  Google Scholar 

  27. T. Johnson, L.V. Lakshmanan and R. Ng (2000). The 3W model and algebra for unified data mining. In Proc. of the Intl. Conf. on Very Large Data Bases, pages 21–32. Morgan Kaufmann, San Francisco, CA.

    Google Scholar 

  28. S. Kramer, L. De Raedt, C. Helma (2001). Molecular feature mining in HIV data. In Proc. 7th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, pages 136–143. ACM Press, New York.

    Google Scholar 

  29. 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 

  30. R. Meo (2003) Optimization of a language for data mining. In Proc. 18th ACM Symposium on Applied Computing, pages 437–444. ACM Press, New York.

    Google Scholar 

  31. T.M. Mitchell (1982). Generalization as search. Artificial Intelligence, 18(2): 203–226.

    Article  MathSciNet  Google Scholar 

  32. S. Nijssen and L. De Raedt. IQL: a proposal for an inductive query language. In 5th Intl. Wshp. on Knowledge Discovery in Inductive Databases: Revised Selected and Invited Papers, pages 189–207. Springer, Berlin.

    Google Scholar 

  33. A. Pečkov, S. Džeroski, and L. Todorovski (2007). Multi-target polynomial regression with constraints. In Proc. Intl. Wshp. on Constrained-Based Mining and Learning, pages 61–72. ECML/PKDD, Warsaw.

    Google Scholar 

  34. R.G. Pensa, C. Robardet, and J-F. Boulicaut (2008). Constraint-driven co-clustering of 0/1 data. In S. Basu, I. Davidson, and K. Wagstaff, editors, Constrained Clustering: Advances in Algorithms, Theory and Applications, pages 145–170. Chapman & Hall/CRC Press, Boca Raton, FL.

    Google Scholar 

  35. K. Wagstaff and C. Cardie (2000). Clustering with instance-level constraints. In Proc. 17th Intl. Conf. on Machine Learning, pages 1103–1110. Morgan Kaufmann, San Francisco, CA.

    Google Scholar 

  36. Q. Yang and X. Wu (2006). 10 Challenging problems in data mining research. International Journal of Information Technology & Decision Making, 5(4): 597–604.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia

    Sašo Džeroski

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  1. Sašo Džeroski
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Correspondence to Sašo Džeroski .

Editor information

Editors and Affiliations

  1. , Department of Knowledge Technologies, Jozef Stefan Institute, Jamova 39, Ljubljana, 1000, Slovenia

    Sašo Džeroski

  2. , Mathematics and Computer Science, University of Antwerp, Middelheimlaan 1, Antwerpen, B-2020, Belgium

    Bart Goethals

  3. , Dept. of Knowledge Technologies, Jožef Stefan Institute, Jamova cesta 39, Ljubljana, SI-1000, Slovenia

    Panče Panov

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Džeroski, S. (2010). Inductive Databases and Constraint-based Data Mining: Introduction and Overview. In: Džeroski, S., Goethals, B., Panov, P. (eds) Inductive Databases and Constraint-Based Data Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7738-0_1

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  • DOI: https://doi.org/10.1007/978-1-4419-7738-0_1

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  • Online ISBN: 978-1-4419-7738-0

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