Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
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.
Y. Bastide, N. Pasquier, R. Taouil, G. Stumme, and L. Lakhal. Mining minimal non-redundant association rules using frequent closed itemsets. In Proc. CL 2000, volume 1861 of LNCS, pages 972–986. Springer-Verlag, 2000.
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.
J.-F. Boulicaut. Inductive databases and multiple uses of frequent itemsets: the clnQ approach. In Database Technologies for Data Mining-Discovering Knowledge with Inductive Queries, volume 2682 of LNCS, pages 3–26. Springer-Verlag, 2004.
J.-F. Boulicaut and B. Jeudy. Constraint-based Data Mining. In Data Mining and Knowledge Discovery Handbook. Chapter 18, this volume, Kluwer, 2005.
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.
T. Calders and B. Goethals. Mining all non-derivable frequent itemsets. In Proc. PKDD, volume 2431 of LNCS, pages 74–85. Springer-Verlag, 2002.
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.
L. De Raedt. A perspective on inductive databases. SIGKDD Explorations, 4(2):69–77, 2003.
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.
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.
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.
T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Communications of the ACM, 39(11):58–64, November 1996.
T. Imielinski and A. Virmani. MSQL: A query langugage for database mining. Data Mining and Knowledge Discovery, 3(4):373–408, 1999.
T. Imielinski, A. Virmani, and A. Abdulghani. DMajor-application programming interface for database mining. Data Mining and Knowledge Discovery, 3(4):347–372, 1999.
B. Jeudy and J.-F. Boulicaut. Optimization of association rule mining queries. Intelligent Data Analysis, 6(4):341–357, 2002.
R. Meo. Optimization of a language for Data Mining. In Proc. ACM SAC03-Data Mining track, pages 437–444, 2003.
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.
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.
K. Morik and M. Scholz. The Mining Mart approach to knowledge discovery in databases. In Intelligent Technologies for Information Analysis. Springer-Verlag, 2004.
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.
R. Ng, L. V. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules. In roc. ACM SIGMOD’98, pages 13–24, 1998.
G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991.
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.
A. Tuzhilin and B. Liu. Querying multiple sets of discovered rules. In Proc. ACMSIGKDDy02, pages 52–60, 2002.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer Science+Business Media, Inc.
About this chapter
Cite this chapter
Boulicaut, JF., Masson, C. (2005). Data Mining Query Languages. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/0-387-25465-X_32
Download citation
DOI: https://doi.org/10.1007/0-387-25465-X_32
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-24435-8
Online ISBN: 978-0-387-25465-4
eBook Packages: Computer ScienceComputer Science (R0)