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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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.
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Table of contents (18 chapters)
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Introduction
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- Panče Panov, Sašo Džeroski, Larisa N. Soldatova
Pages 27-58
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- Hendrik Blockeel, Toon Calders, Élisa Fromont, Bart Goethals, Adriana Prado, Céline Robardet
Pages 59-77
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- Luc De Raedt, Manfred Jaeger, Sau Dan Lee, Heikki Mannila
Pages 79-103
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Constraint-based Mining: Selected Techniques
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Front Matter
Pages 105-105
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- Jérémy Besson, Jean-François Boulicaut, Tias Guns, Siegfried Nijssen
Pages 107-126
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- Björn Bringmann, Siegfried Nijssen, Albrecht Zimmermann
Pages 127-154
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- Jan Struyf, Sašo Džeroski
Pages 155-175
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- Loïc Cerf, Bao Tran Nhan Nguyen, Jean-François Boulicaut
Pages 199-228
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- Luc De Raedt, Angelika Kimmig, Bernd Gutmann, Kristian Kersting, Vítor Santos Costa, Hannu Toivonen
Pages 229-262
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Inductive Databases: Integration Approaches
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Front Matter
Pages 263-263
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- Hendrik Blockeel, Toon Calders, Élisa Fromont, Adriana Prado, Bart Goethals, Céline Robardet
Pages 265-287
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- Jörg Wicker, Lothar Richter, Stefan Kramer
Pages 289-309
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- Arno Siebes, Diyah Puspitaningrum
Pages 311-334
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- Joaquin Vanschoren, Hendrik Blockeel
Pages 335-361
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Applications
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Front Matter
Pages 363-363
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- Celine Vens, Leander Schietgat, Jan Struyf, Hendrik Blockeel, Dragi Kocev, Sašo Džeroski
Pages 365-387
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
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, Dept. of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia
Panče Panov