Constraint-Based Mining and Inductive Databases
European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers
Published on 25. January 2006
Book
Paperback/Softback
X, 404 pages
978-3-540-31331-1 (ISBN)
Description
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.
More details
Series
Edition
2006 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
X, 404 p.
Dimensions
Height: 23.5 cm
Width: 15.5 cm
Weight
1290 gr
ISBN-13
978-3-540-31331-1 (9783540313311)
DOI
10.1007/11615576
Schweitzer Classification
Content
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.