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Data Mining Applications with R By Yanchang Zhao, Yonghua Cen

Download Data Mining Applications with R By Yanchang Zhao, Yonghua Cen

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Sales Rank: #989979 in Books
Brand: Brand: Academic Press
Published on: 2013-12-26
Original language: English
Number of items: 1
Dimensions: 9.25" h x 1.06" w x 7.52" l, 2.55 pounds
Binding: Hardcover
514 pages

Features

Used Book in Good Condition

Review

"The book contains a wealth of modern material that should be covered in more depth in statistics courses: for example, missing data, outlier detection, missing imputation, correlation coefficient matrices, principles of model selection, text mining, and decision trees…The book has many hot and recent packages; many are written or have theory based on results developed since 2010."--MAA.org, April 23, 2014 "Zhao and Cen present 15 real-world applications of data mining with the open-source statistics software R. Each application covers the business background, and problems, data extraction and exploitation, data preprocessing, modeling, model evaluation, findings, and model deployment. They involve a diverse set of challenging problems in terms of data size, data type, data mining goals, and the methodologies and tools to carry out the analysis."--ProtoView.com, February 2014

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.

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