1. Data mining in business -- 2. Business data mining tools -- 3. Data mining processes and knowledge discovery -- 4. Overview of data mining techniques -- 5. Data mining software -- 6. Regression algorithms in data mining -- 7. Neural networks in data mining -- 8. Decision tree algorithms -- 9. Sca...

Ausführliche Beschreibung

Bibliographische Detailangaben

1. Verfasser:	Olson, David L. 1944- (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	New York, New York (222 East 46th Street, New York, NY 10017) Business Expert Press 2016
Ausgabe:	First edition
Schriftenreihe:	Big data and business analytics collection
Schlagworte:	Data mining Business / Data processing
Online-Zugang:	FHN01 FWS01 FWS02 UBY01 Volltext
Zusammenfassung:	1. Data mining in business -- 2. Business data mining tools -- 3. Data mining processes and knowledge discovery -- 4. Overview of data mining techniques -- 5. Data mining software -- 6. Regression algorithms in data mining -- 7. Neural networks in data mining -- 8. Decision tree algorithms -- 9. Scalability -- Notes -- References -- Index Data mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to first describe the benefits of data mining in business, describe the process and typical business applications, describe the workings of basic data mining models, and demonstrate each with widely available free software. The book focuses on demonstrating common business data mining applications. It provides exposure to the data mining process, to include problem identification, data management, and available modeling tools. The book takes the approach of demonstrating typical business data sets with open source software. KNIME is a very easy-to-use tool, and is used as the primary means of demonstration. R is much more powerful and is a commercially viable data mining tool. We will demonstrate use of R through Rattle. We also demonstrate WEKA, which is a highly useful academic software, although it is difficult to manipulate test sets and new cases, making it problematic for commercial use. We will demonstrate methods with a small but typical business dataset. We use a larger (but still small) realistic business dataset for Chapter 9
Beschreibung:	Includes bibliographical references (pages 167-168) and index
Beschreibung:	Online-Ressource (172 pages)
ISBN:	9781631575495 9781631575488