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Dzeroski, Saso (Herausgeber); Goethals, Bart (Herausgeber); Panov, Pane (Herausgeber)

Inductive Databases and Constraint-Based Data Mining (Gebundene Ausgabe)

Springer-Verlag GmbH, Springer US, New York, N.Y., Dezember 2010


456 S. - Sprache: Englisch - 242x167x35 mm

ISBN: 1441977376 EAN: 9781441977373

This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The aim of the book as to provide an overview of the state-of- the art in this novel and - citing research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the uni?cation of pattern mining approaches through constraint programming, the clari?cation of the re- tionship between mining local patterns and global models, and the proposed in- grative frameworks and approaches for inducive databases. On the application side, applications to practically relevant problems from bioinformatics are presented. Inductive databases (IDBs) represent a database view on data mining and kno- edge discovery. IDBs contain not only data, but also generalizations (patterns and models) valid in the data. In an IDB, ordinary queries can be used to access and - nipulate data, while inductive queries can be used to generate (mine), manipulate, and apply patterns and models. In the IDB framework, patterns and models become "?rst-class citizens" and KDD becomes an extended querying process in which both the data and the patterns/models that hold in the data are queried.


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Produktdetails

Provides a broad and unifying perspective on the field of data mining in general and inductive databases in particularIncludes constraint-based mining of predictive models for structured data/outputs, integration/unification of pattern and model mining at the conceptual levelDiscusses applications to practically relevant problems in bioinformatics

Inhaltsverzeichnis

Inductive Databases and Constraint-based Data Mining: Introduction and Overview.- Representing Entities in the OntoDM Data Mining Ontology.- A Practical Comparative Study Of Data Mining Query Languages.- A Theory of Inductive Query Answering.- Constraint-based Mining: Selected Techniques.- Generalizing Itemset Mining in a Constraint Programming Setting.- From Local Patterns to Classification Models.- Constrained Predictive Clustering.- Finding Segmentations of Sequences.- Mining Constrained Cross-Graph Cliques in Dynamic Networks.- Probabilistic Inductive Querying Using ProbLog.- Inductive Databases: Integration Approaches.- Inductive Querying with Virtual Mining Views.- SINDBAD and SiQL: Overview, Applications and Future Developments.- Patterns on Queries.- Experiment Databases.- Applications.- Predicting Gene Function using Predictive Clustering Trees.- Analyzing Gene Expression Data with Predictive Clustering Trees.- Using a Solver Over the String Pattern Domain to Analyze Gene Promoter Sequences.- Inductive Queries for a Drug Designing Robot Scientist.


ISBN 1-441977-37-6, ISBN 1-44-197737-6, ISBN 1-44197-737-6, ISBN 1-441-97737-6, ISBN 1-4419-7737-6

ISBN 978-1-441977-37-3, ISBN 978-1-44-197737-3, ISBN 978-1-44197-737-3, ISBN 978-1-441-97737-3, ISBN 978-1-4419-7737-3

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