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.
R code, Data and color figures for the book are provided at the RDataMining.com website.
- 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
Researchers in academia and industry working in the field of data mining, postgraduate students who are interested in data mining, as well as data miners and analysts from industry. Government agencies, banks, insurance, retail, telecom, medicine and scientific research.
Objectives and Significance
Chapter 1. Power Grid Data Analysis with R and Hadoop
1.2 A Brief Overview of the Power Grid
1.3 Introduction to MapReduce, Hadoop, and RHIPE
1.4 Power Grid Analytical Approach
1.5 Discussion and Conclusions
Chapter 2. Picturing Bayesian Classifiers: A Visual Data Mining Approach to Parameters Optimization
2.2 Related Works
2.3 Motivations and Requirements
2.4 Probabilistic Framework of NB Classifiers
2.5 Two-Dimensional Visualization System
2.6 A Case Study: Text Classification
Chapter 3. Discovery of Emergent Issues and Controversies in Anthropology Using Text Mining, Topic Modeling, and Social Network Analysis of Microblog Content
3.2 How Many Messages and How Many Twitter-Users in the Sample?
3.3 Who Is Writing All These Twitter Messages?
3.4 Who Are the Influential Twitter-Users in This Sample?
3.5 What Is the Community Structure of These Twitter-Users?
3.6 What Were Twitter-Users Writing About During the Meeting?
3.7 What Do the Twitter Messages Reveal About the Opinions of Their Authors?
3.8 What Can Be Discovered in the Less Frequently Used Words in the Sample?
3.9 What Are the Topics That Can Be Algorithmically Discovered in This Sample?
Chapter 4. Text Mining and Network Analysis of Digital Libraries in R
4.2 Dataset Preparation
4.3 Manipulating the
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- © Academic Press 2014
- 12th December 2013
- Academic Press
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A Senior Data Mining Analyst in Australia Government since 2009. Before joining public sector, he was an Australian Postdoctoral Fellow (Industry) in the Faculty of Engineering & Information Technology at University of Technology, Sydney, Australia. His research interests include clustering, association rules, time series, outlier detection and data mining applications and he has over forty papers published in journals and conference proceedings. He is a member of the IEEE and a member of the Institute of Analytics Professionals of Australia, and served as program committee member for more than thirty international conferences.
Senior Data Mining Specialist, Australia
"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