TY  - BOOK
AU  - 
AU  - 
AU  - 
ED  - SpringerLink (Online service)
TI  - Inductive Databases and Constraint-Based Data Mining
SN  - 9781441977380
AV  - QA76.9.D3 
U1  - 005.74 23
PY  - 2010///
CY  - New York, NY
PB  - Springer New York
KW  - Computer science
KW  - Database management
KW  - Data mining
KW  - Artificial intelligence
KW  - Bioinformatics
KW  - Computer Science
KW  - Database Management
KW  - Data Mining and Knowledge Discovery
KW  - Artificial Intelligence (incl. Robotics)
KW  - Computational Biology/Bioinformatics
N1  - 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; ZDB-2-SCS
N2  - 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 ǥ?rst-class citizensǥ and KDD becomes an extended querying process in which both the data and the patterns/models that hold in the data are queried
UR  - http://dx.doi.org/10.1007/978-1-4419-7738-0
ER  -