Knowledge Discovery in Inductive Databases (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
252
Utgivningsdatum
2006-03-01
Upplaga
2006 ed.
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
3Bonchi, Francesco
Illustratör/Fotograf
Bibliographie
Illustrationer
VIII, 252 p.
Dimensioner
234 x 156 x 14 mm
Vikt
377 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISSN
0302-9743
ISBN
9783540332923

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Knowledge Discovery in Inductive Databases (häftad)

Knowledge Discovery in Inductive Databases

4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005, Revised Selected and Invited Papers

av Francesco Bonchi, Jean-Francois Boulicaut
Häftad Engelska, 2006-03-01
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This book presents the thoroughly refereed joint postproceedings of the 4th International Workshop on Knowledge Discovery in Inductive Databases, October 2005. 20 revised full papers presented together with 2 are reproduced here. Bringing together the fields of databases, machine learning, and data mining, the papers address various current topics in knowledge discovery and data mining in the framework of inductive databases such as constraint-based mining, database technology and inductive querying.
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Francesco Bonchi, Jean-Francois Boulicaut

Innehållsförteckning

Invited Papers.- Data Mining in Inductive Databases.- Mining Databases and Data Streams with Query Languages and Rules.- Contributed Papers.- Memory-Aware Frequent k-Itemset Mining.- Constraint-Based Mining of Fault-Tolerant Patterns from Boolean Data.- Experiment Databases: A Novel Methodology for Experimental Research.- Quick Inclusion-Exclusion.- Towards Mining Frequent Queries in Star Schemes.- Inductive Databases in the Relational Model: The Data as the Bridge.- Transaction Databases, Frequent Itemsets, and Their Condensed Representations.- Multi-class Correlated Pattern Mining.- Shaping SQL-Based Frequent Pattern Mining Algorithms.- Exploiting Virtual Patterns for Automatically Pruning the Search Space.- Constraint Based Induction of Multi-objective Regression Trees.- Learning Predictive Clustering Rules.