آر و داده‌کاوی: مثال‌ها و مطالعات موردی

R and DataMining introduces researchers, post-graduate students, and analysts to datamining using R, a free software environment for statistical computing andgraphics. The book provides practical methods for using R in applications fromacademia to industry to extract knowledge from vast amounts of data. Readerswill find this book a valuable guide to the use of R in tasks such asclassification and prediction, clustering, outlier detection, associationrules, sequence analysis, text mining, social network analysis, sentimentanalysis, and more. Data miningtechniques are growing in popularity in a broad range of areas, from banking toinsurance, retail, telecom, medicine, research, and government. This bookfocuses on the modeling phase of the data mining process, also addressing dataexploration and model evaluation. With threein-depth case studies, a quick reference guide, bibliography, and links to awealth of online resources, R and Data Mining is a valuable, practical guide toa powerful method of analysis. KeyFeatures:■ Presentsan introduction into using R for data mining applications, covering mostpopular data mining techniques■ Providescode examples and data so that readers can easily learn the techniques■ Featurescase studies in real-world applications to help readers apply the techniques intheir work Content: Front Matter , Pages i-ii Copyright , Page iv Dedication , Page v List of Figures , Pages xi-xiii List of Abbreviations , Page xv Chapter 1 - Introduction , Pages 1-4 , Yangchang Zhao Chapter 2 - Data Import and Export , Pages 5-9 , Yangchang Zhao Chapter 3 - Data Exploration , Pages 11-25 , Yangchang Zhao Chapter 4 - Decision Trees and Random Forest , Pages 27-40 , Yangchang Zhao Chapter 5 - Regression , Pages 41-50 , Yangchang Zhao Chapter 6 - Clustering , Pages 51-61 , Yangchang Zhao Chapter 7 - Outlier Detection , Pages 63-73 , Yangchang Zhao Chapter 8 - Time Series Analysis and Mining , Pages 75-87 , Yangchang Zhao Chapter 9 - Association Rules , Pages 89-103 , Yangchang Zhao Chapter 10 - Text Mining , Pages 105-122 , Yangchang Zhao Chapter 11 - Social Network Analysis , Pages 123-136 , Yangchang Zhao Chapter 12 - Case Study I: Analysis and Forecasting of House Price Indices , Pages 137-150 , Yangchang Zhao Chapter 13 - Case Study II: Customer Response Prediction and Profit Optimization , Pages 151-179 , Yangchang Zhao Chapter 14 - Case Study III: Predictive Modeling of Big Data with Limited Memory , Pages 181-211 , Yangchang Zhao Chapter 15 - Online Resources , Pages 213-219 , Yangchang Zhao R Reference Card for Data Mining , Pages 221-224 Bibliography , Pages 225-228 General Index , Pages 229-230 Package Index , Page 231 Function Index , Pages 233-234

R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.

Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.

With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis.

• 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

R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. 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 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 Introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. This book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data.