Author: Max Bramer
Edition: 2nd ed. 2013
Binding: Paperback
ISBN: 1447148835
Publisher: Springer
Features:
Edition: 2nd ed. 2013
Binding: Paperback
ISBN: 1447148835
Publisher: Springer
Features:
Principles of Data Mining (Undergraduate Topics in Computer Science)
Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. Search and download computer ebooks Principles of Data Mining (Undergraduate Topics in Computer Science) for free.
Categories: Data mining, Knowledge acquisition (Expert systems), Database management->Congresses. Contributors: Jan Komorowski - Editor. Format: Paperback. Download Principles of Data Mining computer ebooks
Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail. This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble
Principles of Data Mining and Knowledge Discovery: First European Symposium, PKDD '97, Trondheim, Norway, June 24-27, 1997 Proceedings
Principles of Data Mining and Knowledge Discovery: First European Symposium, PKDD '97, Trondheim, Norway, June 24-27, 1997 Proceedings: Jan Komorowski, Jan Zytkow
Categories: Data mining. Contributors: David J. Hand - Author. Format: Hardcover
Categories: Data mining. Contributors: Max Bramer - Author. Format: Paperback
Categories: Data mining, Knowledge acquisition (Expert systems), Database management->Congresses. Contributors: Jan Komorowski - Editor. Format: Paperback
Principles of Data Mining Free
This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble
