[R] New book release: Data Mining Applications with R
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. A preview of the book is available on Google Books at http://books.google.com.au/books?id=nYpqQBAJprintsec=frontcoversource=gbs_ge_summary_rcad=0. R code, data and color figures for the book can be downloaded at http://www.rdatamining.com/books/dmar/code. Buy the book on - Amazon: http://www.amazon.com/Data-Mining-Applications-Yanchang-Zhao/dp/012411511X - Elsevier: http://store.elsevier.com/Data-Mining-Applications-with-R/Yanchang-Zhao/isbn-9780124115118/ - Google Books: http://books.google.com.au/books/about/Data_Mining_Applications_with_R.html?id=nYpqQBAJ 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 Regards Yanchang Zhao PhD, Data Miner RDataMining.com [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] New book announcement: R and Data Mining - Examples and Case Studies
R and Data Mining: Examples and Case Studies Author: Yanchang Zhao Publisher: Academic Press, Elsevier Publish date: December 2012 ISBN: 978-0-12-396963-7 Length: 256 pages URL: http://www.rdatamining.com/books/rdm This book introduces into using R for data mining with examples and case studies. It contains 1) examples on decision trees, random forest, regression, clustering, outlier detection, time series analysis, association rules, text mining and social network analysis; and 2) three real-world case studies. Table of Contents and Abstracts: http://www.rdatamining.com/books/rdm/toc R Code and Data for the book: http://www.rdatamining.com/books/rdm/code Sample pages on Google Books: http://books.google.com.au/books?id=FEOh08LBD9UCprintsec=frontcoversource=gbs_ge_summary_rcad=0#v=onepageqf=false Buy the book on Amazon: http://www.amazon.com/Data-Mining-Examples-Case-Studies/dp/0123969638 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Call for contribution: the RDataMining package - an R package for data mining
Join the RDataMining project to build a comprehensive R package for data mining http://www.rdatamining.com/package We have started the RDataMining project on R-Forge to build an R package for data mining. The package will provide various functionalities for data mining, with contributions from many R users. If you have developed or will implement any data mining algorithms in R, please participate in the project to make your work available to R users worldwide. Background == Although there are many R packages for various data mining functionalities, there are many more new algorithms designed and published every year, without any R implementations for them. It is far beyond the capability of a single team, even several teams, to build packages for oncoming new data mining algorithms. On the other hand, many R users developed their own implementations of new data mining algorithms, but unfortunately, used for their own work only, without sharing with other R users. The reason could be that they donot know or donot have time to build packages to share their code, or they might think that it is not worth building a package with only one or two functions. Objective = To forester the development of data mining capability in R and facilitate sharing of data mining codes/functions/algorithms among R users, we started this project on R-Forge to collaboratively build an R package for data mining, with contributions from many R users, including ourselves. How it works = The project works in a way similar to an edited book. We, as organizers, send out call for participation and solicit R users to join this project and contribute their implemented functions and algorithms. The contributed functions will build up and make a package. Function authors will be responsible for the development, maintenance and documentation of their contributed functions. We will put all functions together as one package and also make a manual for the package. Function authors will be acknowledged as authors of corresponding functions in help documentation and manual of the package. We, as the organizers of the package, will be shown as the manager/maintainer of the whole package. It's free to join or quit the project at any time, and authors can withdraw their contributed functions at any time. Links = The RDataMining package and project: http://www.rdatamining.com/package The RDataMining project on R-Forge: http://package.rdatamining.com or http://r-forge.r-project.org/projects/rdatamining/ Contact === Yanchang Zhao yanchang at rdatamining.com Join the RDataMining Project, and we will work together to build a comprehensive R package for data mining. Regards Yanchang Zhao PhD, Data Miner Email: yanchangz...@gmail.com RDataMining Website:http://www.rdatamining.com RDataMining Package: http://www.rdatamining.com/package RDataMining on Twitter: http://twitter.com/RDataMining Group on LinkedIn: http://group.rdatamining.com __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] 2nd call for chapters: book Data Mining Applications with R; due by 31 May 2012
Book title: Data Mining Applications with R Publisher: Elsevier URL: http://www.rdatamining.com/books/book2 Due date: 2nd round of chapter proposals due by 31 May 2012 Potential authors are expected to submit a 1-2 page manuscript proposal clearly explaining the mission and concerns of the proposed chapter. See details at http://www.rdatamining.com/books/book2. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Call for chapters: Data Mining Applications with R
Book title: Data Mining Applications with R URL: http://www.rdatamining.com/books/book2. Publisher: Elsevier Chapter proposal due date: 30 April 2012 Introduction R is one of the most widely used data mining tools in scientific and business applications, among dozens of commercial and open-source data mining software. It is free and expandable with over 3,600 packages. However, it is not easy for beginners to find appropriate packages or functions to use for their data mining tasks. It is more difficult, even for experienced users, to work out the optimal combination of multiple packages or functions to solve their business problems and the best way to use them in the data mining process of their applications. This book aims to facilitate using R in data mining applications by presenting real-world applications in various areas. Objective - This book will present around 20 applications on data mining with R. Each application is to be presented as one chapter, covering its background, business problems, data extraction and exploration, data preprocessing, modeling, model evaluation, findings and model deployment. In this way, it will help readers to learn to solve real-world problems with a set of data mining techniques and then apply the techniques and methodologies in their own data mining projects. Code examples and sample data will be provided, so that readers can easily learn the techniques by running the codes by themselves. Target audience --- The audience includes data miners, analysts and R users from industry, and university students and researchers who are interested in data mining with R. Topics -- data mining applications with R in, but not limited to, the following areas * Finance * Retail * Insurance * Telecommunications * Government * Crime Homeland Security * Stock Market * Social Welfare * Social Media * Sports * Medicine and Health * Education * Patent * Transport * Real Estate * Meteorology * Bioinformatics * Sentiment Analysis * Spatial Data Analysis * Scientific Computing Submission procedure Data miners and analysts are invited to submit by April 30, 2012, a 1-2 page manuscript proposal clearly explaining the mission and concerns of the proposed chapter. Authors of accepted proposals will be notified by May 15, 2012 about the status of their proposals. Full chapters are due by July 31, 2012. All submitted chapters will be reviewed by 2 or 3 reviewers. Please submit your chapter proposals and full chapters at https://www.easychair.org/account/signin.cgi?conf=dmar2013. Details about the book are available at http://www.rdatamining.com/books/book2. Book editors and contacts - Dr. Yanchang Zhao RDataMining.com, Australia yanchangzhao at gmail dot com Mr. Yonghua Cen Univ. of Technology, Sydney, Australia justin.cen at gmail dot com __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Help: stemming and stem completion with package tm in R
Hi All I came across a problem below when doing stemming and stem completion with package tm in R. Word mining was stemmed to mine with stemDocument(), and then completed to minerswith stemCompletion(). However, I prefer to keep mining intact. For stemCompletion(), the default type of completion is prevalent, which takes the most frequent match as completion. Although mining is much more frequent than miners in my text, it still completed mine to miners. An example is shown below. library(tm) (a - c(mining, miners, mining)) (b - stemDocument(a)) (d - stemCompletion(b, dictionary=a)) Some possible solutions are: 1) to change the options or dictionary in stemDocument(), so that mining is not stemmed to mine, which I think is the best way; 2) to change the options or dictionary in stemCompletion(), so that mine is completed to mining; 3) to manually correct this after stem completion, which is the last option. I am looking for a solution for above 1) or 2), but cannot find the way to do it with stemDocument() in package tm. Any help will be appreciated. Thanks, Yanchang Zhao Email: yanchangzhao(at)gmail.com RDataMining: http://www.rdatamining.com Twitter: http://twitter.com/RDataMining Group on Linkedin: http://group2.rdatamining.com [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Time Series Analysis and Mining with R - slides in PDF
Hi Slides of my talk on Time Series Analysis and Mining with R at Canberra R Users Group on 18 July are available at http://www.rdatamining.com/docs. It presents time series decomposition, forecasting, clustering and classification with R code examples. Regards -- Yanchang Zhao PhD, Data Miner Email: yanch...@rdatamining.com RDataMining: http://www.rdatamining.com Group: http://group.rdatamining.com Twitter: http://www.twitter.com/RDataMining __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Help: which R packages are for time series data mining
Hi Are there any R packages or functions available for data mining of time series data? By mining, I mean representation, similarity metrics, change points detection, classification and clustering of time series data. Thanks Yanchang -- Yanchang Zhao PhD, Data Miner Email: yanch...@rdatamining.com RDataMining: http://www.rdatamining.com __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] R Reference Card for Data Mining
An R Reference Card for Data Mining is available for free download at http://www.rdatamining.com. It can be a quick reference card for you to use R for data mining applications. Regards -- Yanchang Zhao PhD Data Miner Email: yanchangz...@gmail.com RDataMining: http://www.rdatamining.com Twitter: http://www.twitter.com/RDataMining __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
