| 書(shū)目名稱 | Inductive Databases and Constraint-Based Data Mining | | 編輯 | Sa?o D?eroski,Bart Goethals,Pan?e Panov | | 視頻video | http://file.papertrans.cn/464/463881/463881.mp4 | | 概述 | Provides a broad and unifying perspective on the field of data mining in general and inductive databases in particular.Includes constraint-based mining of predictive models for structured data/outputs | | 圖書(shū)封面 |  | | 描述 | 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 que | | 出版日期 | Book 2010 | | 關(guān)鍵詞 | Clustering; Computer; Data mining; Inductive databases; Pattern Mining; bioinformatics; calculus; classific | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4419-7738-0 | | isbn_softcover | 978-1-4899-8217-9 | | isbn_ebook | 978-1-4419-7738-0 | | copyright | Springer Science+Business Media, LLC 2010 |
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