R and Data Mining

Examples and Case Studies

Nonfiction, Science & Nature, Mathematics, Statistics, Computers, General Computing
Cover of the book R and Data Mining by Yanchang Zhao, Elsevier Science
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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 applicationsĀ to help readers apply the techniques in their work
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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