By Roberto J. Bayardo (auth.), Jean-François Boulicaut, Luc De Raedt, Heikki Mannila (eds.)
The 18 articles during this cutting-edge survey current the most recent leads to inductive querying and constraint-based info mining and likewise establish destiny instructions of this newly rising box mendacity on the intersection of knowledge mining and database learn. The papers handle topical sections on foundations of inductive database frameworks, optimizing inductive queries on neighborhood styles, optimizing inductive queries on international styles, and functions of inductive querying techniques.
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Additional 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
Han, S. Chee, and J. Chiand. Issues for On-Line Analytical Mining of Data Warehouses. In Proceedings of DMKD’98. 28. J. Han, Y. Fu, K. Koperski, W. Wang, and O. Zaiane. DMQL: A Data Mining Query Language for Relational Databases. In Proceedings of DMKD’96. 29. J. Han, L. V. S. Lakshmanan, and R. T. Ng. Constraint-based, multidimensional data mining. Computer, 32(8):46–50, 1999. 30. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Proceedings of ACM SIGMOD’00.
Wijsen. Logical Languages for Data Mining. In Logics for emerging Applications of Databases. Springer, Berlin, 2003. 25. G. Grahne, L. Lakshmanan, and X. Wang. Eﬃcient mining of constrained correlated sets. In Proceedings of ICDE’00). 26. J. Han. Towards On-Line Analytical Mining in Large Databases. Sigmod Records, 27(1):97–107, 1998. 27. J. Han, S. Chee, and J. Chiand. Issues for On-Line Analytical Mining of Data Warehouses. In Proceedings of DMKD’98. 28. J. Han, Y. Fu, K. Koperski, W. Wang, and O.
Example 14 (Embedding the primitive in a SQL-like language). Consider the constraint-based frequent pattern query of Example 13. The following is some SQL-like syntactic sugar to express such query. MINE PATTERNS Freq_pat, Support FROM sales GROUPING item BY day,cust MINIMUM SUPPORT: 3 CONSTRAINTS ON name FROM product HAVING SUM(price) >= 30, In the ﬁrst line we deﬁne the name of the two output attributes Freq pat and Support corresponding to the variables I and S of the query in Example 13. In the FROM clause we indicate the data source, or, in other words, the relation which plays the role of the mining view (V): note that according to Deﬁnition 5 the mining view can be deﬁned by a relational expression; this means that in the FROM clause we can have a full SQL SELECT clause.