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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.
- 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.
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R code, Data and color figures for the book are provided at the RDataMining.com website.
Table of Contents
- 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, Mar�a Jes�s B�rcena and Patricia Men�ndez
- 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.
Most helpful customer reviews
18 of 21 people found the following review helpful.
Not sure what to call it...
By T. Yokota
The book is filled with interesting applications of R and data mining; however, I am unsure where to place it in my library. Each chapter is a brief summary of the process taken to solve a question with data, which is peppered with R syntax and figures. Given that each chapter is a case study, I found many decisions to be given in one-liner explanations. Thankfully, most of the contributing authors provide references for further exploration.
What I found to be the most limiting factor from enjoying the material was the preparation of the text and supplemental materials. For example, chapter 8 syntax does not include syntax on how to import the CSV file nor how to use some packages/functions as detailed in the chapter. Granted it may be assumed the reader should know this ahead of time, I feel for the sake of consistency that such information should still be provided. Consequently, the syntax feels more like a sketchbook rather than a step-by-step process on following the author towards a solution. For those reading this review, you can visit the following link and compare chapter 8 code and dataset - some familiarity with R may be needed to identify the discrepancy: [...]
I was hoping this book would be a great addition to my library and for others, but I cannot justify recommending this book as of now. I hope the authors of the book revisit the supplemental material and text to ensure consistency in the reading.
4 of 4 people found the following review helpful.
Not worth it !!!
By Appan Ponnappan
I was expecting a great deal since it has real life applications with R source code for trying out the different algorithms. But I was deeply disappointed with the typesetting of the document - R commands are mixed up with their outputs, there is no consistency of the R code & output formatting, across the different chapters and worse, the source code in the accompanying web site for some of the chapters are either incomplete (for example Ch.13, fortify.R is missing) or existing files are not syntactically correct (Chapter 8). I have been spending the last few days on running the source code to reproduce the results given in the book but I have succeeded in running 2 chapters.
I am not sure how reputed publishers such as AP can compromise on quality & do a lousy job and the publisher would have been better of by suggesting the authors of the various chapters to use the R-package knitr and that would have brought out the formatting consistency automatically and avoided authors pasting source code into the text document processor & making avoidable mistakes !!!
Content-wise also, not all the applications are interesting. Chapters 6, 11, 12, 14 - all of them use random forest to analyse the data-sets. Not all the chapters are interesting for the reader who is specialising in specific domain. At least they could have chosen the case-studies & the data sets in a way that a wider set of algorithms are covered.
2 of 2 people found the following review helpful.
case-studies are very good for novices of data mining, but editing is poor.
By hxy0135 NJ
The contents(case-studies) are very good for people who are not so experienced in data mining field. I had hoped I could learn advanced data mining via step-by-step case analysis. But many R codes of the book cannot run and some dataset referenced in the R code cannot be found in the data set download. I hope the author revisit the supplemental material and R code to make them connsistent and provide updates via rdatamining.com.
See all 5 customer reviews...
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