Inductive Databases and Constraint-Based Data Mining (eBook)

Sašo Džeroski, Bart Goethals, Panče Panov (Herausgeber)

eBook Download: PDF

2010 | 2010
XVII, 456 Seiten
Springer New York (Verlag)
978-1-4419-7738-0 (ISBN)

Infos

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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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 "e;?rst-class citizens"e; and KDD becomes an extended querying process in which both the data and the patterns/models that hold in the data are queried.

Inhalt

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

Produkt-Details

• Erscheint lt. Verlag: 18.11.2010
• Zusatzinfo: XVII, 456 p.
• Verlagsort: New York
• Sprache: englisch
• Themenwelt: Informatik ► Datenbanken ► Data Warehouse / Data Mining
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Naturwissenschaften ► Biologie
• Schlagworte: Bioinformatics • Calculus • classification • Clustering • Computer • Computer Science • Database • Data Mining • inductive databases • machine learning • pattern mining • predictive models • programming • structured data
• ISBN-10: 1-4419-7738-4 / 1441977384
• ISBN-13: 978-1-4419-7738-0 / 9781441977380