Data Mining Applications with R

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9780124115118: Data Mining Applications with R

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

From the Author:

This book presents 15 real-world applications on data mining with R. Each application is presented as one chapter, covering business background and problems, data extraction and exploration, data preprocessing, modeling, model evaluation, findings and model deployment.
 
R code, Data and color figures for the book are provided at the RDataMining.com website.

Table of Contents

  • Foreword
    Graham Williams
  • Chapter 1 Power Grid Data Analysis with R and Hadoop
    Terence Critchlow, Ryan Hafen, Tara Gibson and Kerstin Kleese van Dam
  • Chapter 2 Picturing Bayesian Classifiers: A Visual Data Mining Approach to Parameters Optimization
    Giorgio Maria Di Nunzio and Alessandro Sordoni
  • Chapter 3 Discovery of emergent issues and controversies in Anthropology using text mining, topic modeling and social network analysis of microblog content
    Ben Marwick
  • Chapter 4 Text Mining and Network Analysis of Digital Libraries in R
    Eric Nguyen
  • Chapter 5 Recommendation systems in R
    Saurabh Bhatnagar
  • Chapter 6 Response Modeling in Direct Marketing: A Data Mining Based Approach for Target Selection
    Sadaf Hossein Javaheri, Mohammad Mehdi Sepehri and Babak Teimourpour
  • Chapter 7 Caravan Insurance Policy Customer Profile Modeling with R Mining
    Mukesh Patel and Mudit Gupta
  • Chapter 8 Selecting Best Features for Predicting Bank Loan Default
    Zahra Yazdani, Mohammad Mehdi Sepehri and Babak Teimourpour
  • Chapter 9 A Choquet Ingtegral Toolbox and its Application in Customer's Preference Analysis
    Huy Quan Vu, Gleb Beliakov and Gang Li
  • Chapter 10 A Real-Time Property Value Index based on Web Data
    Fernando Tusell, Maria Blanca Palacios, María Jesús Bárcena and Patricia Menéndez
  • Chapter 11 Predicting Seabed Hardness Using Random Forest in R
    Jin Li, Justy Siwabessy, Zhi Huang, Maggie Tran and Andrew Heap
  • Chapter 12 Supervised classification of images, applied to plankton samples using R and zooimage
    Kevin Denis and Philippe Grosjean
  • Chapter 13 Crime analyses using R
    Madhav Kumar, Anindya Sengupta and Shreyes Upadhyay
  • Chapter 14 Football Mining with R
    Maurizio Carpita, Marco Sandri, Anna Simonetto and Paola Zuccolotto
  • Chapter 15 Analyzing Internet DNS(SEC) Traffic with R for Resolving Platform Optimization
    Emmanuel Herbert, Daniel Migault, Stephane Senecal, Stanislas Francfort and Maryline Laurent

From the Back Cover:

Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R in solving different problems in industry. Fifteen different real world case studies in hot topic areas are presented offering various techniques from experts in the field. With R becoming the most popular and widely used statistical software in learning data mining techniques across many different industries, this book is an essential resource for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool.

"Sobre este título" puede pertenecer a otra edición de este libro.

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