Browse content
Table of contents
Actions for selected chapters
- Full text access
- Book chapterAbstract only
Chapter 1 - Power Grid Data Analysis with R and Hadoop
Pages 1-34 - Book chapterAbstract only
Chapter 2 - Picturing Bayesian Classifiers: A Visual Data Mining Approach to Parameters Optimization
Pages 35-61 - Book chapterAbstract only
Chapter 3 - Discovery of Emergent Issues and Controversies in Anthropology Using Text Mining, Topic Modeling, and Social Network Analysis of Microblog Content
Pages 63-93 - Book chapterAbstract only
Chapter 4 - Text Mining and Network Analysis of Digital Libraries in R
Pages 95-115 - Book chapterAbstract only
Chapter 5 - Recommender Systems in R
Pages 117-151 - Book chapterAbstract only
Chapter 6 - Response Modeling in Direct Marketing: A Data Mining-Based Approach for Target Selection
Pages 153-180 - Book chapterAbstract only
Chapter 7 - Caravan Insurance Customer Profile Modeling with R
Pages 181-227 - Book chapterAbstract only
Chapter 8 - Selecting Best Features for Predicting Bank Loan Default
Pages 229-245 - Book chapterAbstract only
Chapter 9 - A Choquet Integral Toolbox and Its Application in Customer Preference Analysis
Pages 247-272 - Book chapterAbstract only
Chapter 10 - A Real-Time Property Value Index Based on Web Data
Pages 273-297 - Book chapterAbstract only
Chapter 11 - Predicting Seabed Hardness Using Random Forest in R
Pages 299-329 - Book chapterAbstract only
Chapter 12 - Supervised Classification of Images, Applied to Plankton Samples Using R and Zooimage
Pages 331-365 - Book chapterAbstract only
Chapter 13 - Crime Analyses Using R
Pages 367-395 - Book chapterAbstract only
Chapter 14 - Football Mining with R
Pages 397-433 - Book chapterAbstract only
Chapter 15 - Analyzing Internet DNS(SEC) Traffic with R for Resolving Platform Optimization
Pages 435-456 - Book chapterNo access
Index
Pages 457-470
About the book
Description
Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R, a free software environment for statistical computing and graphics, in solving different problems in industry. R is widely used in leveraging data mining techniques across many different industries, including government, finance, insurance, medicine, scientific research and more. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. It is an ideal companion for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool.
R code, Data and color figures for the book are provided at the RDataMining.com website.
Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R, a free software environment for statistical computing and graphics, in solving different problems in industry. R is widely used in leveraging data mining techniques across many different industries, including government, finance, insurance, medicine, scientific research and more. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. It is an ideal companion for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool.
R code, Data and color figures for the book are provided at the RDataMining.com website.
Key Features
- Helps data miners to learn to use R in their specific area of work and see how R can apply in different industries
- Presents various case studies in real-world applications, which will help readers to apply the techniques in their work
- Provides code examples and sample data for readers to easily learn the techniques by running the code by themselves
- Helps data miners to learn to use R in their specific area of work and see how R can apply in different industries
- Presents various case studies in real-world applications, which will help readers to apply the techniques in their work
- Provides code examples and sample data for readers to easily learn the techniques by running the code by themselves
Details
ISBN
978-0-12-411511-8
Language
English
Published
2014
Copyright
Copyright © 2013 Elsevier Inc. All rights reserved.
Imprint
Academic Press