Inductive Databases and Constraint-Based Data Mining [recurso electrónico] / edited by Sašo Džeroski, Bart Goethals, Pance Panov.

Por: Džeroski, Sašo [editor.]Colaborador(es): Goethals, Bart [editor.] | Panov, Pance [editor.] | SpringerLink (Online service)Tipo de material: TextoTextoEditor: New York, NY : Springer New York, 2010Descripción: XVII, 456 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9781441977380Tema(s): Computer science | Database management | Data mining | Artificial intelligence | Bioinformatics | Computer Science | Database Management | Data Mining and Knowledge Discovery | Artificial Intelligence (incl. Robotics) | Computational Biology/BioinformaticsFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 005.74 Clasificación LoC:QA76.9.D3Recursos en línea: Libro electrónicoTexto
Contenidos:
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.
En: Springer eBooksResumen: 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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Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos QA76.9 .D3 (Browse shelf(Abre debajo)) 1 No para préstamo 372011-2001

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.

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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