Inductive Databases and Constraint-Based Data Mining

. Ed(s): Dzeroski, Saso; Goethals, Bart; Panov, Pance

ISBN 10: 1441977376 / ISBN 13: 9781441977373

Published by Springer-Verlag New York Inc., 2010

About this Item

This presentation of emergent research topics at the intersection of data mining and database research covers the state of the art in this field as well as examining its applications to problems of practical relevance such as those in bioinformatics. Editor(s): Dzeroski, Saso; Goethals, Bart; Panov, Pance. Num Pages: 456 pages, biography. BIC Classification: PSA; UNF; UYQ. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 26. Weight in Grams: 840. . 2010. 2010th Edition. hardcover. . . . . Books ship from the US and Ireland.

Seller Inventory # V9781441977373

Bibliographic Details

• Title: Inductive Databases and Constraint-Based Data Mining
• Publisher: Springer-Verlag New York Inc.
• Publication Date: 2010
• Language: English
• ISBN 10: 1441977376
• ISBN 13: 9781441977373
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.

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