Principles Of Data Mining

Bramer, Max

ISBN 10: 1846287650 / ISBN 13: 9781846287657

Published by Springer London Ltd, 2007

About this Item

Attention: Ancien support de bibliothèque, plastifié, étiquettes. Merci, votre achat aide à financer des programmes de lutte contre l'illettrisme.

Seller Inventory # 7211201705262ZAD11846287650

Bibliographic Details

• Title: Principles Of Data Mining
• Publisher: Springer London Ltd
• Publication Date: 2007
• Language: English
• ISBN 10: 1846287650
• ISBN 13: 9781846287657
• Binding: Soft cover
• Condition: Bon

About this title

Synopsis

This book explains the principal techniques of data mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples and explanations of the algorithms given. This will benefit readers of all levels, from those who use data mining via commercial packages, right through to academic researchers. The book aims to help the general reader develop the necessary understanding to use commercial data mining packages, and to enable advanced readers to understand or contribute to future technical advances. Includes exercises and glossary.

From the Back Cover

Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. This book explains and explores the principal techniques of Data Mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples & explanations of the algorithms given. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help the general reader develop the necessary understanding to use commercial data mining packages discriminatingly, as well as enabling the advanced reader or academic researcher to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included.