Abstract
Knowledge discovery in databases (KDD) is a process that can include steps like forming the data set, data transformations, discovery of patterns, searching for exceptions to a pattern, zooming on a subset of the data, and postprocessing some patterns. We describe a comprehensive framework in which all these steps can be carried out by means of queries over an inductive database. An inductive database is a database that in addition to data also contains intensionally defined generalizations about the data. We formalize this concept: an inductive database consists of a normal database together with a subset of patterns from a class of patterns, and an evaluation function that tells how the patterns occur in the data. Then, looking for potential query languages built on top of SQL, we consider the research on the MINE RULE operator by Meo, Psaila and Ceri. It is a serious step towards an implementation framework for inductive databases, though it addresses only the association rule mining problem. Perspectives are then discussed.
On sabbatical leave from INSA Lyon (F). This work is partly supported by AFFRST, Association Franco-Finlandaise pour la Recherche Scientifique et Technique.
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R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In SIGMOD’93, pages 207–216. ACM, 1993.
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.
J.-F. Boulicaut, M. Klemettinen, and H. Mannila. Modeling KDD processes within the inductive database framework. Technical Report C-1998-29, Department of Computer Science, P.O. Box 26, FIN-00014 University of Helsinki, Finland, June 1998. Submitted.
L. Dehaspe and L. D. Raedt. Mining association rules in multiple relations. In ILP’97, volume 1297 of LNAI, pages 125–132. Springer-Verlag, 1997.
L. Dehaspe and H. Toivonen. Frequent query discovery: a unifying ILP approach to association rule mining. Technical Report CW-258, Department of Computer Science, Katholieke Universiteit Leuven, Belgium, March 1998.
U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors. Advances in Knowledge Discovery and Data Mining. AAAI Press, 1996.
B. Goethals, J. V. den Bussche, and K. Vanhoof. Decision support queries for the interpretation of data mining results. Manuscript. University of Limburg (Belgium), available at http://www.luc.ac.be/tvdbuss, 1998.
J. M. Hellerstein. Optimization techniques for queries with expensive methods. ACM Transaction on Database Systems, 1998. To appear.
T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Communications of the ACM, 39(11):58–64, Nov. 1996.
G. Lausen and G. Vossen. Models and Languages of Object-Oriented Databases. Addison-Wesley Publishing Company, 1997.
H. Mannila. Inductive databases and condensed representations for data mining. In ILPS’97, pages 21–30. MIT Press, 1997.
H. Mannila. Methods and problems in data mining. In ICDT’97, volume 1186 of LNCS, pages 41–55, Springer-Verlag, 1997.
H. Mannila and H. Toivonen. Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery, 1(3):241–258, 1997.
R. Meo, G. Psaila, and S. Ceri. A new SQL-like operator for mining association rules. In VLDB’96, pages 122–133. Morgan Kaufmann, 1996.
R. Meo, G. Psaila, and S. Ceri. A tightly-coupled architecture for data mining. In ICDE’98, pages 316–322. IEEE Computer Society Press, 1998.
R. Ng, L. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules. In SIGMOD’98, pages 13–24. ACM, 1998.
H. Toivonen, M. Klemettinen, P. Ronkainen, K. Hätönen, and H. Mannila. Pruning and grouping of discovered association rules. In Workshop Notes of the ECML-95 Workshop on Statistics, Machine Learning, and Knowledge Discovery in Databases, pages 47–52. MLnet, 1995.
D. Tsur, J. D. Ullman, S. Abiteboul, C. Clifton, R. Motwani, S. Nestorov, and A. Rozenthal. Query flocks: A generalization of association-rule mining. In SIGMOD’98, pages 1–12. ACM, 1998.
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Boulicaut, JF., Klemettinen, M., Mannila, H. (1998). Querying inductive databases: A case study on the MINE RULE operator. In: Żytkow, J.M., Quafafou, M. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1998. Lecture Notes in Computer Science, vol 1510. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0094820
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DOI: https://doi.org/10.1007/BFb0094820
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