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.
Inhaltsverzeichnis
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.
