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Inductive Databases and Constraint-Based Data Mining
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 t...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer New York : Imprint: Springer,
2010.
|
| Edición: | 1st ed. 2010. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 008 | 101118s2010 xxu| s |||| 0|eng d | ||
| 020 | |a 9781441977380 |9 978-1-4419-7738-0 | ||
| 024 | 7 | |a 10.1007/978-1-4419-7738-0 |2 doi | |
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| 245 | 1 | 0 | |a Inductive Databases and Constraint-Based Data Mining |h [electronic resource] / |c edited by Sašo Džeroski, Bart Goethals, Panče Panov. |
| 250 | |a 1st ed. 2010. | ||
| 264 | 1 | |a New York, NY : |b Springer New York : |b Imprint: Springer, |c 2010. | |
| 300 | |a XVII, 456 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Database management. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Bioinformatics. | |
| 650 | 1 | 4 | |a Database Management. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Computational and Systems Biology. |
| 700 | 1 | |a Džeroski, Sašo. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Goethals, Bart. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Panov, Panče. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9781441977373 |
| 776 | 0 | 8 | |i Printed edition: |z 9781489982179 |
| 776 | 0 | 8 | |i Printed edition: |z 9781441977397 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-1-4419-7738-0 |z Texto Completo |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||