R and Data Mining

Examples and Case Studies

Yanchang Zhao (Autor)

Buch | Hardcover

256 Seiten

2013
Academic Press Inc (Verlag)
978-0-12-396963-7 (ISBN)

Artikel merken

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.

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.

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.

Introduction

Introduction, Data mining

R
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

Erscheint lt. Verlag	31.1.2013
Verlagsort	San Diego
Sprache	englisch
Maße	152 x 229 mm
Gewicht	570 g
Themenwelt	Informatik ► Datenbanken ► Data Warehouse / Data Mining
	Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
	Mathematik / Informatik ► Mathematik
ISBN-10	0-12-396963-8 / 0123969638
ISBN-13	978-0-12-396963-7 / 9780123969637
Zustand	Neuware