Inductive Databases and Constraint-Based Data Mining (Hardcover)

Bart Goethals

ISBN 10: 1441977376 / ISBN 13: 9781441977373

Editore: Springer-Verlag New York Inc., New York, NY, 2010

Riguardo questo articolo

Hardcover. 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. This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Codice articolo 9781441977373

Dati bibliografici

• Titolo: Inductive Databases and Constraint-Based Data Mining (Hardcover)
• Editore: Springer-Verlag New York Inc., New York, NY
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 1441977376
• ISBN 13: 9781441977373
• Rilegatura: Hardcover
• Condizione: new

Descrizione articolo

Riassunto

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.

Contenuti

Inductive Databases and Constraint-based Data Mining: Introduction and Overview.- Representing Entities in the OntoDM Data Mining Ontology.- A Practical Comparative Study Of Data Mining Query Languages.- A Theory of Inductive Query Answering.- Constraint-based Mining: Selected Techniques.- Generalizing Itemset Mining in a Constraint Programming Setting.- From Local Patterns to Classification Models.- Constrained Predictive Clustering.- Finding Segmentations of Sequences.- Mining Constrained Cross-Graph Cliques in Dynamic Networks.- Probabilistic Inductive Querying Using ProbLog.- Inductive Databases: Integration Approaches.- Inductive Querying with Virtual Mining Views.- SINDBAD and SiQL: Overview, Applications and Future Developments.- Patterns on Queries.- Experiment Databases.- Applications.- Predicting Gene Function using Predictive Clustering Trees.- Analyzing Gene Expression Data with Predictive Clustering Trees.- Using a Solver Over the String Pattern Domain to Analyze Gene Promoter Sequences.- Inductive Queries for a Drug Designing Robot Scientist.