By Roberto J. Bayardo (auth.), Jean-François Boulicaut, Luc De Raedt, Heikki Mannila (eds.)
The 18 articles during this state of the art survey current the most recent ends up in inductive querying and constraint-based info mining and likewise establish destiny instructions of this newly rising box mendacity on the intersection of information mining and database examine. The papers deal with topical sections on foundations of inductive database frameworks, optimizing inductive queries on neighborhood styles, optimizing inductive queries on worldwide styles, and functions of inductive querying techniques.
Read or Download Constraint-Based Mining and Inductive Databases: European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers PDF
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Extra resources for Constraint-Based Mining and Inductive Databases: European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers
Rigotti. Query driven knowledge discovery in multidimensional data. In Proceedings of DOLAP’99. 15. C. Bucila, J. Gehrke, D. Kifer, and W. White. DualMiner: A dual-pruning algorithm for itemsets with constraints. In Proceedings of ACM SIGKDD’02. 16. S. Choenni and A. Siebes. Query Optimization to Support Data Mining. In Proc. of the Int’l. Workshop on Database and Expert Systems Application 1997. 17. L. Dehaspe and L. De Raedt. Dlab: A declarative language bias formalism. In Proceedings of ISMIS’96.
Consider again the relation in Figure 1(a) and (c). 4 with exactly two items in the head (one of type beverage and one of type snack) and at least 3 items in the body. As usually we can divide the association rule mining problem in two parts: the mining of frequent itemsets and the subsequent generation of valid rules. For the ﬁrst subproblem we can use an inductive query which exploits our primitive in order to have an eﬃcient mining, while the second post-processing subproblem can be solved by a simple DATALOG query.
The MIT Press, 2001. 32. M. Houtsma and A. Swami. Set-oriented mining for association rules in relational databases. In Proceedings of ICDE’95. 33. T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Comm. Of The Acm, 39:58–64, 1996. 34. T. Imielinski and A. Virmani. MSQL: A Query Language for Database Mining. Data Mining and Knowledge Discovery, 3(4):373–408, 1999. 35. T. Imielinski, A. Virmani, and A. Abdulghani. DMajor - Application Programming Interface for Database Mining.
Constraint-Based Mining and Inductive Databases: European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers by Roberto J. Bayardo (auth.), Jean-François Boulicaut, Luc De Raedt, Heikki Mannila (eds.)