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Foreword
Preface to the third edition
Preface to the first edition
Acknowledgments

PART ⅠPRELIMINARIES
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 (k-NN)
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

References
Data Files Used in the Book
Index

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data.

Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes:

·Real-world examples to build a theoretical and practical understanding of key data mining methods
·End-of-chapter exercises that help readers better understand the presented material
·Data-rich case studies to illustrate various applications of data mining techniques
·Completely new chapters on social network analysis and text mining
·A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides
·Free 140-day license to use XLMiner for Education software

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.