Librería Portfolio

Features

Covers the main data mining techniques through carefully selected case studies
Describes code and approaches that can be easily reproduced or adapted to your own problems
Requires no prior experience with R or ggplot
Includes introductions to R and MySQL basics
Provides a fundamental understanding of the merits, drawbacks, and analysis objectives of the data mining techniques
Provides code, datasets, and other supplements on the companion website: http://ltorgo.github.io/DMwR2
Summary

Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R.

The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document.

The book is accompanied by a set of freely available R source files that can be obtained at the book's web site. These files include all the code used in the case studies, and they facilitate the ´do-it-yourself´ approach followed in the book.

Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the ´world´ of R and data mining.



Table of Contents

Introduction

I R AND DATA MINING

Introduction to R

Starting with R

Basic Interaction with the R Console

R Objects and Variables

R Functions

Vectors

Vectorization

Factors

Generating Sequences

Sub-Setting

Matrices and Arrays

Lists

Data Frames

Useful Extensions to Data Frames

Objects, Classes, and Methods

Managing Your Sessions

Introduction to Data Mining

A Bird's Eye View on Data Mining

Data Collection and Business Understanding

Data Pre-Processing

Modeling

Evaluation

Reporting and Deployment

II CASE STUDIES

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 Seven Algae

Summary

Predicting Stock Market Returns

Problem Description and Objectives

The Available Data

The Prediction Models

From Predictions into Actions

Model Evaluation and Selection

The Trading System

Summary

Detecting Fraudulent Transactions

Problem Description and Objectives

The Available Data

Defining the Data Mining Tasks

Obtaining Outlier Rankings

Summary

Classifying Microarray Samples

Problem Description and Objectives

The Available Data

Gene (Feature) Selection

Predicting Cytogenetic Abnormalities

Summary