Book title: Data Mining Applications with R

Editors: Yanchang Zhao, Yonghua Cen

Publisher: Elsevier

Publish date: December 2013

ISBN: 978-0-12-411511-8

Length: 514 pages

URL: http://www.rdatamining.com/books/dmar

An edited book titled Data Mining Applications with R was released in December 2013, which features 15 real-word applications on data mining with R.

Book preview on Google Books

R code, data and color figures for the book

Buy the book on

– Amazon

– Elsevier

– Google Books

Below is its 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*

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Hello Yanchang ,

I suspect this post would greatly benefit from adding some pictures to it.

If you will do so – e-mail me so I could update the post on r-bloggers.

Good luck with your new book, it looks very interesting 🙂

With regards,

Tal

Thank you for your suggestion. I have added some pictures to the blog post. Could you please update the corresponding page on r-bloggers?

Thanks and have a happy holiday.

Reblogged this on Drug and other Products Counterfeits Mining.

Hi,

I am new in data mining. and Hoped that I can learn advanced data mining via step-by-step fashion by following the book samples. The samples presented in the book are all good examples. But many R code don’t run. and some dataset mentioned in R cannot be found or in different format. some Amonzon readers hope you can provide R and data set updates. Is it possible to provide a more consistent supplimental material via RDataMining.com?

Thank you,

My apologies for inconsistency in code/data formatting and possible errors in them, and thanks for your suggestion. I am working on that and hopefully will provide updated versions of code/data soon after contacting chapter authors.

Thank you for your reply.

I just updated my review on amazon and gave it 4 stars. I like the book’s style – learning by doing