Inductive databases and constraint-based data mining / 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 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. Front Matter....Pages 1-15 Front Matter....Pages 1-1 Inductive Databases and Constraint-based Data Mining: Introduction and Overview....Pages 3-26 Representing Entities in the OntoDM Data Mining Ontology....Pages 27-58 A Practical Comparative Study Of Data Mining Query Languages....Pages 59-77 A Theory of Inductive Query Answering....Pages 79-103 Front Matter....Pages 105-105 Generalizing Itemset Mining in a Constraint Programming Setting....Pages 107-126 From Local Patterns to Classification Models....Pages 127-154 Constrained Predictive Clustering....Pages 155-175 Finding Segmentations of Sequences....Pages 177-197 Mining Constrained Cross-Graph Cliques in Dynamic Networks....Pages 199-228 Probabilistic Inductive Querying Using ProbLog....Pages 229-262 Front Matter....Pages 263-263 Inductive Querying with Virtual Mining Views....Pages 265-287 SINDBAD and SiQL: Overview, Applications and Future Developments....Pages 289-309 Patterns on Queries....Pages 311-334 Experiment Databases....Pages 335-361 Front Matter....Pages 363-363 Predicting Gene Function using Predictive Clustering Trees....Pages 365-387 Analyzing Gene Expression Data with Predictive Clustering Trees....Pages 389-406 Using a Solver Over the String Pattern Domain to Analyze Gene Promoter Sequences....Pages 407-423 Inductive Queries for a Drug Designing Robot Scientist....Pages 425-451 Back Matter....Pages 454-457 This book presents inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The book provides an overview of the state-of-the art in this novel research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the unification of pattern mining approaches through constraint programming, the clarification of the relationship between mining local patterns and global models, and the proposed integrative frameworks and approaches for inductive databases. On the application side, applications to practically relevant problems from bioinformatics are presented to attract additional attention from a wider audience. The primary audience consists of scientists and graduate students in computer science and bio-informatics. Potential readers are likely to attend conferences on databases, data mining/ machine learning, and bio-informatics.