Overview
Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 600)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (17 chapters)
Keywords
About this book
Knowledge discovery (KDD) and Data Mining (DM) is a new, multidisciplinary field that focuses on the overall process of information discovery from large volumes of data. The field combines database concepts and theory, machine learning, pattern recognition, statistics, artificial intelligence, uncertainty management, and high-performance computing.
To remain competitive, businesses must apply data mining techniques such as classification, prediction, and clustering using tools such as neural networks, fuzzy logic, and decision trees to facilitate making strategic decisions on a daily basis.
Knowledge Discovery for Business Information Systems contains a collection of 16 high quality articles written by experts in the KDD and DM field from the following countries: Austria, Australia, Bulgaria, Canada, China (Hong Kong), Estonia, Denmark, Germany, Italy, Poland, Singapore and USA.
Editors and Affiliations
Bibliographic Information
Book Title: Knowledge Discovery for Business Information Systems
Editors: Witold Abramowicz, Jozef Zurada
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/b116447
Publisher: Springer New York, NY
-
eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2002
Hardcover ISBN: 978-0-7923-7243-1Published: 30 November 2000
Softcover ISBN: 978-1-4757-7475-7Published: 07 April 2013
eBook ISBN: 978-0-306-46991-6Published: 18 April 2006
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XVIII, 432
Topics: Data Structures and Information Theory, IT in Business, Artificial Intelligence