Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration
Readers will learn
how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities.
This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:
Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government
Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students
More than a dozen case studies demonstrating applications for the data mining techniques described
End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
“ This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.”
Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R
Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 publications including books.
Peter C. Bruce is President and Founder of the Institute for Statistics Education at Statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective (Wiley) and co-author of Practical Statistics for Data Scientists: 50 Essential Concepts (O’Reilly).
Inbal Yahav, PhD, is Professor at the Graduate School of Business Administration at Bar-Ilan University, Israel. She teaches courses in social network analysis, advanced research methods, and software quality assurance. Dr. Yahav received her PhD in Operations Research and Data Mining from the University of Maryland, College Park.
Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years.
Kenneth C. Lichtendahl, Jr., PhD, is Associate Professor at the University of Virginia. He is the Eleanor F. and Phillip G. Rust Professor of Business Administration and teaches MBA courses in decision analysis, data analysis and optimization, and managerial quantitative analysis. He also teaches executive education courses in strategic analysis and decision-making, and managing the corporate aviation function.
Table of Contents
PART I PRELIMINARIES
CHAPTER 1 Introduction
CHAPTER 2 Overview of the Data Mining Process
PART II DATA EXPLORATION AND DIMENSION REDUCTION
CHAPTER 3 Data Visualization
CHAPTER 4 Dimension Reduction
PART III PERFORMANCE EVALUATION
CHAPTER 5 Evaluating Predictive Performance
PART IV PREDICTION AND CLASSIFICATION METHODS
CHAPTER 6 Multiple Linear Regression
CHAPTER 7 k-Nearest Neighbors (kNN)
CHAPTER 8 The Naive Bayes Classifier
CHAPTER 9 Classification and Regression Trees
CHAPTER 10 Logistic Regression
CHAPTER 11 Neural Nets
CHAPTER 12 Discriminant Analysis
CHAPTER 13 Combining Methods: Ensembles and Uplift Modeling
PART V MINING RELATIONSHIPS AMONG RECORDS
CHAPTER 14 Association Rules and Collaborative Filtering
CHAPTER 15 Cluster Analysis
PART VI FORECASTING TIME SERIES
CHAPTER 16 Handling Time Series
CHAPTER 17 Regression-Based Forecasting
CHAPTER 18 Smoothing Methods
PART VII DATA ANALYTICS
CHAPTER 19 Social Network Analytics
CHAPTER 20 Text Mining
PART VIII CASES
CHAPTER 21 Cases