Data Mining Applications with R

✍ Scribed by Yanchang Zhao and Justin Cen (Auth.)

• Publisher: Academic Press
• Year: 2013
• Leaves: 470
• Edition: 1
• Category: Library

✦ Synopsis

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. The book

• 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

R code, Data and color figures for the book are provided at the RDataMining.com website.

✦ Table of Contents

Content:
Front Matter, Pages i-ii
Copyright, Page iv
Preface, Pages xiii-xiv
Acknowledgments, Page xv
Review Committee, Pages xvii-xviii
Foreword, Pages xix-xxi
Chapter 1 - Power Grid Data Analysis with R and Hadoop, Pages 1-34
Chapter 2 - Picturing Bayesian Classifiers: A Visual Data Mining Approach to Parameters Optimization, Pages 35-61
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
Chapter 4 - Text Mining and Network Analysis of Digital Libraries in R, Pages 95-115
Chapter 5 - Recommender Systems in R, Pages 117-151
Chapter 6 - Response Modeling in Direct Marketing: A Data Mining-Based Approach for Target Selection, Pages 153-180
Chapter 7 - Caravan Insurance Customer Profile Modeling with R, Pages 181-227
Chapter 8 - Selecting Best Features for Predicting Bank Loan Default, Pages 229-245
Chapter 9 - A Choquet Integral Toolbox and Its Application in Customer Preference Analysis, Pages 247-272
Chapter 10 - A Real-Time Property Value Index Based on Web Data, Pages 273-297
Chapter 11 - Predicting Seabed Hardness Using Random Forest in R, Pages 299-329
Chapter 12 - Supervised Classification of Images, Applied to Plankton Samples Using R and Zooimage, Pages 331-365
Chapter 13 - Crime Analyses Using R, Pages 367-395
Chapter 14 - Football Mining with R, Pages 397-433
Chapter 15 - Analyzing Internet DNS(SEC) Traffic with R for Resolving Platform Optimization, Pages 435-456
Index, Pages 457-470

✦ Subjects

Библиотека;Компьютерная литература;R;