Inductive Databases and Constraint-Based Data Mining

1. Front Matter

   Pages 1-15

   PDF

2. Introduction

   1. Front Matter

      Pages 1-1

      PDF

   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

      PDF

   2. Predicting Gene Function using Predictive Clustering Trees

      • Celine Vens, Leander Schietgat, Jan Struyf, Hendrik Blockeel, Dragi Kocev, Sašo Džeroski

      Pages 365-387

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

• , 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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