Constraint-Based Mining and Inductive Databases

Mannila Heikki De Raedt Luc Boulicaut Jean-Francois

ISBN 10: 3540313311 / ISBN 13: 9783540313311

Editore: Springer, 2006

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pp. 420.

Codice articolo 18299199

Dati bibliografici

• Titolo: Constraint-Based Mining and Inductive Databases
• Editore: Springer
• Data di pubblicazione: 2006
• Lingua: Inglese
• ISBN 10: 3540313311
• ISBN 13: 9783540313311
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

The interconnected ideas of inductive databases and constraint-based mining are appealing and have the potential to radically change the theory and practice of data mining and knowledge discovery. This book reports on the results of the European IST project "cInQ" (consortium on knowledge discovery by Inductive Queries) and its final workshop entitled Constraint-Based Mining and Inductive Databases organized in Hinterzarten, Germany in March 2004.

Contenuti

The Hows, Whys, and Whens of Constraints in Itemset and Rule Discovery.- A Relational Query Primitive for Constraint-Based Pattern Mining.- To See the Wood for the Trees: Mining Frequent Tree Patterns.- A Survey on Condensed Representations for Frequent Sets.- Adaptive Strategies for Mining the Positive Border of Interesting Patterns: Application to Inclusion Dependencies in Databases.- Computation of Mining Queries: An Algebraic Approach.- Inductive Queries on Polynomial Equations.- Mining Constrained Graphs: The Case of Workflow Systems.- CrossMine: Efficient Classification Across Multiple Database Relations.- Remarks on the Industrial Application of Inductive Database Technologies.- How to Quickly Find a Witness.- Relevancy in Constraint-Based Subgroup Discovery.- A Novel Incremental Approach to Association Rules Mining in Inductive Databases.- Employing Inductive Databases in Concrete Applications.- Contribution to Gene Expression Data Analysis by Means of Set Pattern Mining.- Boolean Formulas and Frequent Sets.- Generic Pattern Mining Via Data Mining Template Library.- Inductive Querying for Discovering Subgroups and Clusters.