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Inductive Databases and Constraint-Based Data Mining. 2010 ed.
・ISBN 978-1-4899-8217-9 paper EUR 99.99
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| 著者・編者 | Dzeroski, Saso / Goethals, Bart / Panov, Pance (eds.), |
|---|---|
| 出版社 | (Springer-Verlag New York Inc., US) |
| 出版年月 | 2014 |
| ページ数 | 456 pp. |
| 言語 | ENG |
| ニュース番号 | <A05-51530> |
解説
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.