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Data Mining and Knowledge Discovery Handbook pp 655–664Cite as

Data Mining Query Languages

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Summary

Many Data Mining algorithms enable to extract different types of patterns from data (e.g., local patterns like itemsets and association rules, models like classifiers). To support the whole knowledge discovery process, we need for integrated systems which can deal either with patterns and data. The inductive database approach has emerged as an unifying framework for such systems. Following this database perspective, knowledge discovery processes become querying processes for which query languages have to be designed. In the prolific field of association rule mining, different proposals of query languages have been made to support the more or less declarative specification of both data and pattern manipulations. In this chapter, we survey some of these proposals. It enables to identify nowadays shortcomings and to point out some promising directions of research in this area.

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References

  • R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, pages 307– 328. AAAI Press, 1996.

    Google Scholar 

  • Y. Bastide, N. Pasquier, R. Taouil, G. Stumme, and L. Lakhal. Mining minimal nonredundant association rules using frequent closed itemsets. In Proc. CL 2000, volume 1861 of LNCS, pages 972–986. Springer-Verlag, 2000.

    Google Scholar 

  • M. Botta, J.-F. Boulicaut, C. Masson, and R. Meo. Query languages supporting descriptive rule mining: a comparative study. In Database Technologies for Data Mining - Discovering Knowledge with Inductive Queries, volume 2682 of LNCS, pages 27–54. Springer-Verlag, 2004.

    Google Scholar 

  • J.-F. Boulicaut. Inductive databases and multiple uses of frequent itemsets: the cInQ approach. In Database Technologies for Data Mining - Discovering Knowledge with Inductive Queries, volume 2682 of LNCS, pages 3–26. Springer-Verlag, 2004.

    Google Scholar 

  • J.-F. Boulicaut and B. Jeudy. Constraint-based Data Mining. In Data Mining and Knowledge Discovery Handbook. Chapter 16.7, this volume, Kluwer, 2005.

    Google Scholar 

  • J.-F. Boulicaut, M. Klemettinen, and H. Mannila. Modeling KDD processes within the inductive database framework. In Proc. DaWaK’99, volume 1676 of LNCS, pages 293–302. Springer-Verlag, 1999.

    Google Scholar 

  • T. Calders and B. Goethals. Mining all non-derivable frequent itemsets. In Proc. PKDD, volume 2431 of LNCS, pages 74–85. Springer-Verlag, 2002.

    Google Scholar 

  • B. Catania, A. Maddalena, M. Mazza, E. Bertino, and S. Rizzi. A framework for Data Mining pattern management. In Proc. PKDD’04, volume 3202 of LNAI, pages 87–98. Springer-Verlag, 2004.

    Google Scholar 

  • L. De Raedt. A perspective on inductive databases. SIGKDD Explorations, 4(2):69–77, 2003.

    Article  Google Scholar 

  • L. De Raedt, M. Jaeger, S. Lee, and H. Mannila. A theory of inductive query answering. In Proc. IEEE ICDM’02, pages 123–130, 2002.

    Google Scholar 

  • F. Giannotti and G. Manco. Querying inductive databases via logic-based user-defined aggregates. In Proc. PKDD’99, volume 1704 of LNCS, pages 125–135. Springer-Verlag, 1999.

    Google Scholar 

  • J. Han, Y. Fu,W.Wang, K. Koperski, and O. Zaiane. DMQL: a Data Mining query language for relational databases. In R. Ng, editor, Proc. ACM SIGMOD Workshop DMKD’96, Montreal, Canada, 1996.

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • T. Imielinski, A. Virmani, and A. Abdulghani. DMajor-application programming interface for database mining. Data Mining and Knowledge Discovery, 3(4):347–372, 1999.

    Article  Google Scholar 

  • B. Jeudy and J.-F. Boulicaut. Optimization of association rule mining queries. Intelligent Data Analysis, 6(4):341–357, 2002.

    MATH  Google Scholar 

  • R. Meo. Optimization of a language for Data Mining. In Proc. ACM SAC’03 - Data Mining track, pages 437–444, 2003.

    Google Scholar 

  • R. Meo, P. L. Lanzi, and M. Klemettinen, editors. Database Technologies for Data Mining - Discovering Knowledge with Inductive Queries, volume 2682 of LNCS. Springer-Verlag, 2004.

    Google Scholar 

  • R. Meo, G. Psaila, and S. Ceri. An extension to SQL for mining association rules. Data Mining and Knowledge Discovery, 2(2):195–224, 1998.

    Article  Google Scholar 

  • K. Morik and M. Scholz. The Mining Mart approach to knowledge discovery in databases. In Intelligent Technologies for Information Analysis. Springer-Verlag, 2004.

    Google Scholar 

  • A. Netz, S. Chaudhuri, J. Bernhardt, and U. Fayyad. Integration of Data Mining and relational databases. In Proc. VLDB’00, pages 719–722, Cairo, Egypt, 2000. Morgan Kaufmann.

    Google Scholar 

  • R. Ng, L. V. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules. In Proc. ACM SIGMOD’98, pages 13–24, 1998.

    Google Scholar 

  • G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991.

    Google Scholar 

  • Y. Theodoridis and P. Vassiliadis, editors. Proc. of Pattern Representation and Management PaRMa 2004 co-located with EDBT 2004. CEUR Workshop Proceedings 96 Technical University of Aachen (RWTH), 2004.

    Google Scholar 

  • A. Tuzhilin and B. Liu. Querying multiple sets of discovered rules. In Proc. ACM SIGKDD’02, pages 52–60, 2002.

    Google Scholar 

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Acknowledgments

The authors want to thank the colleagues of the cInQ IST-2000-26469 (consortium on knowledge discovery by inductive queries) for interesting discussions on Data Mining query languages. A special thank goes to Rosa Meo for her contribution to this domain and the critical evaluation (Botta et al., 2004).

Author information

Authors and Affiliations

  1. INSA Lyon, LIRIS CNRS FRE 2672, 69621, Villeurbanne cedex, France

    Jean-Francois Boulicaut & Cyrille Masson

Authors
  1. Jean-Francois Boulicaut
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  2. Cyrille Masson
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Corresponding author

Correspondence to Jean-Francois Boulicaut .

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

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Boulicaut, JF., Masson, C. (2009). Data Mining Query Languages. 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_33

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  • DOI: https://doi.org/10.1007/978-0-387-09823-4_33

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-09822-7

  • Online ISBN: 978-0-387-09823-4

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