This book provides a self-contained introduction to the use of R for exploratory data mining and machine learning. Employing a practical, learn-by-doing approach, the author presents a series of representative case studies from ecology, financial prediction, fraud detection, and bioinformatics, including all of the necessary steps, code, and data. These examples demonstrate how to address important data mining issues, such as handling data sets with too many variables, and illustrate key concepts, including outlier detection and semisupervised learning. A supporting web page provides additional code and data for further study.

About the Author

Luis Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA.

Table of Contents

Introduction How to Read This Book A Short Introduction to R A Short Introduction to MySQL Predicting Algae Blooms Problem Description and Objectives Data Description Loading the Data into R Data Visualization and Summarization Unknown Values Obtaining Prediction Models Model Evaluation and Selection Predictions for the 7 Algae Predicting Stock Market Returns Problem Description and Objectives The Available Data Defining the Prediction Tasks The Prediction Models From Predictions into Actions Model Evaluation and Selection The Trading System Detecting Fraudulent Transactions Problem Description and Objectives The Available Data Defining the Data Mining Tasks Obtaining Outlier Rankings Classifying Microarray Samples Problem Description and Objectives The Available Data Gene (Feature) Selection Predicting Cytogenetic Abnormalities Bibliography Index Index of Data Mining Topics Index of R Functions

Reviews

This is certainly one of the best books for a direct implementation of data mining algorithms. Another good point of the book is that for most of the problems there are different ways to solve them. ! an invaluable resource for data miners, R programmers, as well as people involved in fields such as fraud detection and stock market prediction. If you're serious about data mining and want to learn from experiences in the field, don't hesitate! --Sandro Saitta, Data Mining Research blog, May 2011 If you want to learn how to analyze your data with a free software package that has been built by expert statisticians and data miners, this is your book. A broad range of real-world case studies highlights the breadth and depth of the R software. --Bernhard Pfahringer, University of Waikato, New Zealand Both R novices and experts will find this a great reference for data mining. --Intelligent Trading blog and R-bloggers, November 2010