상세정보

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.

Luis Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He　teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. 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.

If you want to learn how to analyze your data with R, this is your book. A broad range of real-world case studies highlights the breadth and depth of the R software. This expanded second edition delves deeper into topical explanations and updates and expands all case studies. Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools.

정보제공 :

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

정보제공 :