Download Constraint-Based Mining and Inductive Databases: European by Jean-Francois Boulicaut, Luc De Raedt, Heikki Mannila PDF

By Jean-Francois Boulicaut, Luc De Raedt, Heikki Mannila

The interconnected rules of inductive databases and constraint-based mining are attractive and feature the capability to seriously change the idea and perform of information mining and data discovery. This e-book experiences at the result of the eu IST venture "cInQ" (consortium on wisdom discovery through Inductive Queries) and its ultimate workshop entitled Constraint-Based Mining and Inductive Databases organized in Hinterzarten, Germany in March 2004.

The 18 articles during this state of the art survey current the most recent ends up in inductive querying and constraint-based information mining and likewise determine destiny instructions of this newly rising box mendacity on the intersection of knowledge mining and database study. 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 strategies.

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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

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On closed constrained frequent pattern mining. In Proceedings of ICDM’04. 11. F. Bonchi and C. Lucchese. On Condensed Representations of Constrained Frequent Patterns. To appear in Knowledge and Information Systems - An International Journal (KAIS). Springer, Berlin. 12. F. Bonchi and C. Lucchese. Pushing tougher constraints in frequent pattern mining. In Proceedings of PAKDD’05, 2005. 13. F. Boulicaut and B. Jeudy. Using constraints during set mining: Should we prune or not? In Actes des Seizime Journes Bases de Donnes Avances BDA’00.

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Zhang, M. Cho, H. Wang, and P. Yu. Maple: A fast algorithm for maximal pattern-based clustering. In Proceedings of ICDM’03. 51. L. De Raedt. A logical database mining query language. In Proceedings of ILP’00. 52. S. Sarawagi, S. Thomas, and R. Agrawal. Integrating association rule mining with relational database systems: Alternatives and implications. In Proceedings of the ACM SIGMOD’98. 53. W. Shen and B. Leng. A Metapattern-Based Discovery Loop for Integrated Data Mining - Unsupervised Learning of Relational Patterns.

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