Overview
- Editors:
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Sašo Džeroski
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, Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia
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Bart Goethals
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, Mathematics and Computer Science, University of Antwerp, Antwerpen, Belgium
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Panče Panov
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, Dept. of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia
- Provides a broad and unifying perspective on the field of data mining in general and inductive databases in particular
- Includes constraint-based mining of predictive models for structured data/outputs, integration/unification of pattern and model mining at the conceptual level
- Discusses applications to practically relevant problems in bioinformatics
- Includes supplementary material: sn.pub/extras
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Table of contents (18 chapters)
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Applications
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- Ivica Slavkov, Sašo Džeroski
Pages 389-406
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- Christophe Rigotti, Ieva Mitašiūnaitė, Jérémy Besson, Laurène Meyniel, Jean-François Boulicaut, Olivier Gandrillon
Pages 407-423
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- Ross D. King, Amanda Schierz, Amanda Clare, Jem Rowland, Andrew Sparkes, Siegfried Nijssen et al.
Pages 425-451
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Back Matter
Pages 454-457
About this book
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.
Editors and Affiliations
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, Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia
Sašo Džeroski
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, Mathematics and Computer Science, University of Antwerp, Antwerpen, Belgium
Bart Goethals
-
, Dept. of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia
Panče Panov
