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Inductive Databases and Constraint-Based Data Mining

  • Book
  • © 2010

Overview

Editors:
  1. Sašo Džeroski

    1. , Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia

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  2. Bart Goethals

    1. , Mathematics and Computer Science, University of Antwerp, Antwerpen, Belgium

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    You can also search for this editor in PubMed   Google Scholar

  3. Panče Panov

    1. , Dept. of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia

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    You can also search for this editor in PubMed   Google Scholar

  • 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)

  1. Front Matter

    Pages 1-15
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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. Inductive Databases and Constraint-based Data Mining: Introduction and Overview

      • Sašo Džeroski
      Pages 3-26

    3. Representing Entities in the OntoDM Data Mining Ontology

      • Panče Panov, Sašo Džeroski, Larisa N. Soldatova
      Pages 27-58

    4. A Practical Comparative Study Of Data Mining Query Languages

      • Hendrik Blockeel, Toon Calders, Élisa Fromont, Bart Goethals, Adriana Prado, Céline Robardet
      Pages 59-77

    5. A Theory of Inductive Query Answering

      • 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
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    2. Generalizing Itemset Mining in a Constraint Programming Setting

      • Jérémy Besson, Jean-François Boulicaut, Tias Guns, Siegfried Nijssen
      Pages 107-126

    3. From Local Patterns to Classification Models

      • Björn Bringmann, Siegfried Nijssen, Albrecht Zimmermann
      Pages 127-154

    4. Constrained Predictive Clustering

      • Jan Struyf, Sašo Džeroski
      Pages 155-175

    5. Finding Segmentations of Sequences

      • Ella Bingham
      Pages 177-197

    6. Mining Constrained Cross-Graph Cliques in Dynamic Networks

      • Loïc Cerf, Bao Tran Nhan Nguyen, Jean-François Boulicaut
      Pages 199-228

    7. Probabilistic Inductive Querying Using ProbLog

      • 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
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    2. Inductive Querying with Virtual Mining Views

      • Hendrik Blockeel, Toon Calders, Élisa Fromont, Adriana Prado, Bart Goethals, Céline Robardet
      Pages 265-287

    3. SINDBAD and SiQL: Overview, Applications and Future Developments

      • Jörg Wicker, Lothar Richter, Stefan Kramer
      Pages 289-309

    4. Patterns on Queries

      • Arno Siebes, Diyah Puspitaningrum
      Pages 311-334

    5. Experiment Databases

      • Joaquin Vanschoren, Hendrik Blockeel
      Pages 335-361

  5. Applications

    1. Front Matter

      Pages 363-363
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    2. Predicting Gene Function using Predictive Clustering Trees

      • Celine Vens, Leander Schietgat, Jan Struyf, Hendrik Blockeel, Dragi Kocev, Sašo Džeroski
      Pages 365-387
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Keywords

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

  • , Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia

    Sašo Džeroski

  • , Mathematics and Computer Science, University of Antwerp, Antwerpen, Belgium

    Bart Goethals

  • , Dept. of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia

    Panče Panov

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