Data Mining with R Learning with Case Studies, Second Edition

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. About the Author Luís 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.

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

Luís 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.