Dzeroski / Goethals / Panov Inductive Databases and Constraint-Based Data Mining

1. Auflage 2010
ISBN: 978-1-4419-7738-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 456 Seiten

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.

Autoren/Hrsg.

Dzeroski, Saso

Goethals, Bart

Panov, Pance

Panov, Pan?e

Inhaltsverzeichnis

1;Preface;5
2;Acknowledgements;8
3;List of Reviewers;10
4;Contents;12
5;Introduction;17
5.1;Inductive Databases and Constraint-based Data Mining: Introduction and Overview;18
5.2;Representing Entities in the OntoDM Data Mining Ontology;42
5.3;A Practical Comparative Study Of Data Mining Query Languages;74
5.4;A Theory of Inductive Query Answering;93
6;Constraint-based Mining: Selected Techniques;118
6.1;Generalizing Itemset Mining in a Constraint Programming Setting;119
6.2;From Local Patterns to Classification Models;139
6.3;Constrained Predictive Clustering;167
6.4;Finding Segmentations of Sequences;188
6.5;Mining Constrained Cross-Graph Cliques in Dynamic Networks;209
6.6;Probabilistic Inductive Querying Using ProbLog;239
7;Inductive Databases: Integration Approaches;273
7.1;Inductive Querying with Virtual Mining Views;274
7.2;SINDBAD and SiQL: Overview, Applications and Future Developments;297
7.3;Patterns on Queries;318
7.4;Experiment Databases;342
8;Applications;369
8.1;Predicting Gene Function using Predictive Clustering Trees;370
8.2;Analyzing Gene Expression Data with Predictive Clustering Trees;393
8.3;Using a Solver Over the String Pattern Domain to Analyze Gene Promoter Sequences;411
8.4;Inductive Queries for a Drug Designing Robot Scientist;428
9;Author index;457