© 2010

Inductive Databases and Constraint-Based Data Mining

  • Sašo Džeroski
  • Bart Goethals
  • Panče Panov

Table of contents

  1. Front Matter
    Pages 1-15
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Panče Panov, Sašo Džeroski, Larisa N. Soldatova
      Pages 27-58
    3. Hendrik Blockeel, Toon Calders, Élisa Fromont, Bart Goethals, Adriana Prado, Céline Robardet
      Pages 59-77
    4. Luc De Raedt, Manfred Jaeger, Sau Dan Lee, Heikki Mannila
      Pages 79-103
  3. Constraint-based Mining: Selected Techniques

    1. Front Matter
      Pages 105-105
    2. Jérémy Besson, Jean-François Boulicaut, Tias Guns, Siegfried Nijssen
      Pages 107-126
    3. Björn Bringmann, Siegfried Nijssen, Albrecht Zimmermann
      Pages 127-154
    4. Jan Struyf, Sašo Džeroski
      Pages 155-175
    5. Ella Bingham
      Pages 177-197
    6. Loïc Cerf, Bao Tran Nhan Nguyen, Jean-François Boulicaut
      Pages 199-228
    7. Luc De Raedt, Angelika Kimmig, Bernd Gutmann, Kristian Kersting, Vítor Santos Costa, Hannu Toivonen
      Pages 229-262
  4. Inductive Databases: Integration Approaches

    1. Front Matter
      Pages 263-263
    2. Hendrik Blockeel, Toon Calders, Élisa Fromont, Adriana Prado, Bart Goethals, Céline Robardet
      Pages 265-287
    3. Jörg Wicker, Lothar Richter, Stefan Kramer
      Pages 289-309
    4. Arno Siebes, Diyah Puspitaningrum
      Pages 311-334
    5. Joaquin Vanschoren, Hendrik Blockeel
      Pages 335-361
  5. Applications

    1. Front Matter
      Pages 363-363
    2. Celine Vens, Leander Schietgat, Jan Struyf, Hendrik Blockeel, Dragi Kocev, Sašo Džeroski
      Pages 365-387

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.


Clustering Computer Data mining Inductive databases Pattern Mining bioinformatics calculus classification computer science database machine learning predictive models programming structured data

Editors and affiliations

  • Sašo Džeroski
    • 1
  • Bart Goethals
    • 2
  • Panče Panov
    • 3
  1. 1., Department of Knowledge TechnologiesJozef Stefan InstituteLjubljanaSlovenia
  2. 2., Mathematics and Computer ScienceUniversity of AntwerpAntwerpenBelgium
  3. 3., Dept. of Knowledge TechnologiesJožef Stefan InstituteLjubljanaSlovenia

Bibliographic information