SAS Data Mining

2015. Auflage 2015
ISBN: 978-1-4842-0302-6
Verlag: APRESS

Buch, Englisch, 360 Seiten, PB, Format (B × H): 178 mm x 254 mm

Data mining is used today in many different fields including banking, financial analysis of markets, insurance and private health sectors, education, industrial processes, medicine, biology, bioengineering and telecommunication. But regardless of the field in which it is applied, the core concepts and tasks of data mining do not require nor domain-specific knowledge, nor advanced mathematical treatments. SAS Data Mining presents the most common techniques used in SAS data mining in a simple and easy to understand way using SAS Enterprise Miner, regardless of the specific field you're working in, and without needing to draw on complicated mathematical algorithms. SAS Data Mining therefore describes data mining techniques to you in accessible language and clear, practical, hands-on examples and exercises. Each chapter presents a data mining case study, including the results of the case study, you've built and an interpretation of its results, which is so vital of course to your data mining work.
SAS Data Mining begins with an introduction to data mining data and its distinct phases. You'll then learn how to develop the initial phases which include the selection of information, data exploration, data cleansing, transformation of data, and related issues. After these initial data mining phases, this book goes into practical, hands-on detail on both predictive and descriptive data mining techniques. The predictive techniques you'll learn about cover regression, discriminant analysis, decision trees, neural networks, and other model-based techniques. The descriptive techniques then work with variable dimension reduction techniques, techniques of classification and segmentation (clustering), and exploratory data analysis techniques.

Zielgruppe

Popular/general

Autoren/Hrsg.

Lopez, Cesar

Inhaltsverzeichnis

1. Data Mining Concepts, Techniques, and Systems2. The Selection Phase in the Data Mining Process3. Introducing SAS Enterprise Miner4. The Selection Phase in SAS Enterprise Miner5. The Exploration Phase in Data Mining6. The Exploration Phase with SAS Enterprise Miner7. Cleansing and Transforming Data8. Cleansing and Transforming Data in SAS Enterprise Miner9. Data Mining Phase - Predictive Techniques10. Predictive Techniques with SAS Enterprise Miner11. Decision Trees and Cluster Analysis12. Decision Trees and Cluster Analysis with SAS Enterprise Miner13. Neural Networks with SAS Enterprise Miner

Über Autor(innen)

César Perez Lopez is a Professor at the Department of Statistics and Operations Research at the University of Madrid. César Perez Lopez is also a Mathematician and Economist at the National Statistics Institute (INE) in Madrid, a body which belongs to the Superior Systems and Information Technology Department of the Spanish Government. César also currently works at the Institute for Fiscal Studies in Madrid.