Overview
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2682)
Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
About this book
Data mining from traditional relational databases as well as from non-traditional ones such as semi-structured data, Web data, and scientific databases housing biological, linguistic, and sensor data has recently become a popular way of discovering hidden knowledge.
This book on database support for data mining is developed to approaches exploiting the available database technology, declarative data mining, intelligent querying, and associated issues, such as optimization, indexing, query processing, languages, and constraints. Attention is also paid to the solution of data preprocessing problems, such as data cleaning, discretization, and sampling.
The 16 reviewed full papers presented were carefully selected from various workshops and conferences to provide complete and competent coverage of the core issues. Some papers were developed within an EC funded project on discovering knowledge with inductive queries.
Similar content being viewed by others
Keywords
Table of contents (16 chapters)
-
Database Languages and Query Execution
-
Support for KDD-Process
Editors and Affiliations
Bibliographic Information
Book Title: Database Support for Data Mining Applications
Book Subtitle: Discovering Knowledge with Inductive Queries
Editors: Rosa Meo, Pier Luca Lanzi, Mika Klemettinen
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/b99016
Publisher: Springer Berlin, Heidelberg
-
eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 2004
Softcover ISBN: 978-3-540-22479-2Published: 28 July 2004
eBook ISBN: 978-3-540-44497-8Published: 28 July 2004
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XII, 332
Topics: Artificial Intelligence, Database Management, Information Storage and Retrieval