Inductive Databases and Constraint-Based Data Mining

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Džeroski, Sašo (Επιμελητής έκδοσης), Goethals, Bart (Επιμελητής έκδοσης), Panov, Panče (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: New York, NY : Springer New York, 2010.
• Θέματα: Computer science.; Database management.; Data mining.; Artificial intelligence.; Bioinformatics.; Computer Science.; Database Management.; Data Mining and Knowledge Discovery.; Artificial Intelligence (incl. Robotics).; Computational Biology/Bioinformatics.

 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. 

 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.