R and Data Mining
Examples and Case Studies
Nonfiction, Science & Nature, Mathematics, Statistics, Computers, General Computing
| Author: | Yanchang Zhao | ISBN: | 9780123972712 |
| Publisher: | Elsevier Science | Publication: | December 31, 2012 |
| Imprint: | Academic Press | Language: | English |
| Author: | Yanchang Zhao |
| ISBN: | 9780123972712 |
| Publisher: | Elsevier Science |
| Publication: | December 31, 2012 |
| Imprint: | Academic Press |
| Language: | English |
R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.
Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.
With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis.
- Presents an introduction into using R for data mining applications, covering most popular data mining techniques
- Provides code examples and data so that readers can easily learn the techniques
- Features case studies in real-world applications to help readers apply the techniques in their work
R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.
Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.
With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis.
- Presents an introduction into using R for data mining applications, covering most popular data mining techniques
- Provides code examples and data so that readers can easily learn the techniques
- Features case studies in real-world applications to help readers apply the techniques in their work
