TY - BOOK ID - 8434487 TI - Inductive databases and constraint-based data mining AU - Dzeroski, Saso AU - Goethals, Bart. AU - Panov, Pance. PY - 2010 SN - 1489982175 1441977376 9786612980923 1441977384 1282980920 PB - New York : Springer, DB - UniCat KW - Bioinformatics. KW - Data mining -- Congresses. KW - Data mining. KW - Database management -- Congresses. KW - Database searching -- Congresses. KW - Knowledge acquisition (Expert systems). KW - Data mining KW - Engineering & Applied Sciences KW - Computer Science KW - Database management. KW - Data searching. KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Data base management KW - Data services (Database management) KW - Database management services KW - DBMS (Computer science) KW - Generalized data management systems KW - Services, Database management KW - Systems, Database management KW - Systems, Generalized database management KW - Computer science. KW - Artificial intelligence. KW - Computer Science. KW - Database Management. KW - Data Mining and Knowledge Discovery. KW - Artificial Intelligence (incl. Robotics). KW - Computational Biology/Bioinformatics. KW - Database searching KW - Electronic data processing KW - Artificial Intelligence. KW - Bio-informatics KW - Biological informatics KW - Biology KW - Information science KW - Computational biology KW - Systems biology KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Data processing UR - https://www.unicat.be/uniCat?func=search&query=sysid:8434487 AB - 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. ER -