Download Constraint-Based Mining and Inductive Databases: European by Roberto J. Bayardo (auth.), Jean-François Boulicaut, Luc De PDF

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 newest 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 study. The papers tackle topical sections on foundations of inductive database frameworks, optimizing inductive queries on neighborhood styles, optimizing inductive queries on worldwide styles, and purposes of inductive querying techniques.

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Extra info 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

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In the third line we indicate that transactions are created grouping the attribute item by the two attributes day,cust. In other word, we indicate the attribute which plays the role of item (I), and a list of attributes which play the role of transaction identifier (T ). Note that such grouping is not really performed by the underlying DBMS: this is just syntactic sugar to express the input parameters for our primitive. In the fourth line we define the minimum support threshold σ. The last three lines define the additional constraint on the sum of prices.

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. 31. D. Hand, H. Mannila, and P. Smyh. Principles of Data Mining. 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.

Dually, p is called parent of c, denoted as p = π(c). If there is a path from a node a to a node d, a is called an ancestor of d, and d is called a descendant of a. Hence, the root node of a tree is an ancestor of all other nodes in the tree. We use π ∗ (d) =def {π(d)} ∪ π ∗ (π(d)) to denote the set of all ancestors of a node d. For a tree with order k we write ktree. In this work we concentrate onto rooted trees, free trees are not investigated. Hence, we simply use tree to denote a rooted tree.

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