R and Data Mining Examples and Case Studies

Langue : Anglais

Auteur : Zhao Yanchang

Couverture de l’ouvrage R and Data Mining

Résumé
Sommaire
Lectorat
Biographie
Commentaire

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.

Introduction

Introduction, Data mining

Datasets used in this book

Data Loading and Exploration

Data Import/Export

Save/Load R Data

Import from and Export to .CSV Files

Import Data from SAS

Import/Export via ODBC

Data Exploration

Have a Look at Data

Explore Individual Variables

Explore Multiple Variables

More Exploration

Save Charts as Files

Data Mining Examples

Decision Trees

Building Decision Trees with Package party

Building Decision Trees with Package rpart

Random Forest

Regression

Linear Regression

Logistic Regression

Generalized Linear Regression

Non-linear Regression

Clustering

K-means Clustering

Hierarchical Clustering

Density-based Clustering

Outlier Detection

Time Series Analysis

Time Series Decomposition

Time Series Forecast

Association Rules

Sequential Patterns

Text Mining

Social Network Analysis

Case Studies

Case Study I: Analysis and Forecasting of House Price Indices

Reading Data from a CSV File

Data Exploration

Time Series Decomposition

Time Series Forecasting

Discussion

Case Study II: Customer Response Prediction

Case Study III: Risk Rating using Decision Tree with Limited Resources

Customer Behaviour Prediction and Intervention

Appendix

Online Resources

R Reference Card for Data Mining

Bibliography

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. Since data mining techniques are widely used in government agencies, banks, insurance, retail, telecom, medicine and research, the book will be interesting to many areas.

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.

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