Data Mining and Predictive Analytics, 2nd Edition

Learn methods of data analysis and their application toreal-world data sets This updated second edition serves as an introduction to datamining methods and models, including association rules, clustering,neural networks, logistic regression, and multivariate analysis.The authors apply a unified “white box” approach todata mining methods and models. This approach is designed to walkreaders through the operations and nuances of the various methods,using small data sets, so readers can gain an insight into theinner workings of the method under review. Chapters provide readerswith hands-on analysis problems, representing an opportunity forreaders to apply their newly-acquired data mining expertise tosolving real problems using large, real-world data sets. Data Mining and Predictive Analytics, Second Edition: * Offers comprehensive coverage of association rules, clustering,neural networks, logistic regression, multivariate analysis, and Rstatistical programming language * Features over 750 chapter exercises, allowing readers to assesstheir understanding of the new material * Provides a detailed case study that brings together the lessonslearned in the book * Includes access to the companion website,www.dataminingconsultant, with exclusive password-protectedinstructor content

Data Mining and Predictive Analytics, SecondEdition will appeal to computer science and statisticstudents, as well as students in MBA programs, and chiefexecutives.

Table of Contents

Part I: Data Preparation
Chapter 1: An Introduction to Data Mining and Predictive Analytics
Chapter 2: Data Preprocessing
Chapter 3: Exploratory Data Analysis
Chapter 4: Dimension-Reduction Methods

Part II: Statistical Analysis
Chapter 5: Univariate Statistical Analysis
Chapter 6: Multivariate Statistics
Chapter 7: Preparing to Model the Data
Chapter 8: Simple Linear Regression
Chapter 9: Multiple Regression and Model Building

Part III: Classification
Chapter 10: k-Nearest Neighbor Algorithm
Chapter 11: Decision Trees
Chapter 12: Neural Networks
Chapter 13: Logistic Regression
Chapter 14: NaÏVe Bayes and Bayesian Networks
Chapter 15: Model Evaluation Techniques
Chapter 16: Cost-Benefit Analysis Using Data-Driven Costs
Chapter 17: Cost-Benefit Analysis for Trinary and -Nary Classification Models
Chapter 18: Graphical Evaluation of Classification Models

Part IV: Clustering
Chapter 19: Hierarchical and -Means Clustering
Chapter 20: Kohonen Networks
Chapter 21: BIRCH Clustering
Chapter 22: Measuring Cluster Goodness

Part V: Association Rules
Chapter 23: Association Rules

Part VI: Enhancing Model Performance
Chapter 24: Segmentation Models
Chapter 25: Ensemble Methods: Bagging and Boosting
Chapter 26: Model Voting and Propensity Averaging

Part VII: Further Topics
Chapter 27: Genetic Algorithms
Chapter 28: Imputation of Missing Data

Part VIII: Case Study: Predicting Response to Direct-Mail Marketing
Chapter 29: Case Study, Part 1: Business Understanding, Data Preparation, and EDA
Chapter 30: Case Study, Part 2: Clustering and Principal Components Analysis
Chapter 31: Case Study, Part 3: Modeling And Evaluation For Performance And Interpretability
Chapter 32: Case Study, Part 4: Modeling and Evaluation for High Performance Only

Appendix A: Data Summarization and Visualization