R and data mining : examples and case studies / Yanchang Zhao.

This book introduces using R for data mining. Data mining techniques are widely used in government agencies, banks, insurance, retail, telecom, medicine and research. Recently, there is an increasing tendency to do data mining with R, a free software environment for statistical computing and graphics. According to a poll by KDnuggets.com in early 2011, R is the 2nd popular tool for data mining work. By introducing using R for data mining, this book will have a broad audience from both academia and industry. It targets researchers in the field of data mining, postgraduate students who are interested in data mining, and data miners and analysts from industry. For example, many universities have courses on data mining, and the proposed book will be a useful reference for students learning data mining in those courses. There are also many training courses on data mining in industry, such as training by SAS and IBM on data mining. The book will be of interest to the course learners as well. Presents an introduction into using R for data mining applications, covering most popular data mining techniques. Provides code examples and data so that readers can easily learn the techniques. Features case studies in real-world applications to help readers apply the techniques in their work.

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Includes bibliographical references and indexes.

Data import and export -- Data exploration -- Decision trees and random forest -- Regression -- Clustering -- Outlier detection -- Time series analysis and mining -- Association rules -- Text mining -- Social network analysis -- Case study 1. Analysis and forecasting of house price indices -- Case study 2. Customer response prediction and profit optimization -- Case study 3. Predictive modeling of big data with limited memory -- Online resources.

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