Data Mining: A Tutorial-Based Primer, 2nd Edition

“Dr. Roiger does an excellent job of describing in step by step detail formulae involved in various data mining algorithms, along with illustrations. In addition, his tutorials in Weka software provide excellent grounding for students in comprehending the underpinnings of Machine Learning as applied to Data Mining. The inclusion of RapidMiner software tutorials and examples in the book is also a definite plus since it is one of the most popular Data Mining software platforms in use today.”

–Robert Hughes, Golden Gate University, San Francisco, CA, USA

Data Mining: A Tutorial-Based Primer, Second Edition

provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well-known software tools.

Several new topics have been added to the second edition including an introduction to Big Data and data analytics, ROC curves, Pareto lift charts, methods for handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining. The second edition contains tutorials for attribute selection, dealing with imbalanced data, outlier analysis, time series analysis, mining textual data, and more.

The text provides in-depth coverage of RapidMiner Studio and Weka’s Explorer interface. Both software tools are used for stepping students through the tutorials depicting the knowledge discovery process. This allows the reader maximum flexibility for their hands-on data mining experience.

Table of Contents

Section I: Data Mining Fundamentals
Chapter 1. Data Mining: A First View
Chapter 2. Data Mining: A Closer Look
Chapter 3. Basic Data Mining Techniques

Section II: Tools for Knowledge Discovery
Chapter 4. Weka—An Environment for Knowledge Discovery
Chapter 5. Knowledge Discovery with RapidMiner
Chapter 6. The Knowledge Discovery Process
Chapter 7. Formal Evaluation Techniques

Section III: Building Neural Networks
Chapter 8. Neural Networks
Chapter 9. Building Neural Networks with Weka
Chapter 10. Building Neural Networks with RapidMiner

Section IV: Advanced Data Mining Techniques
Chapter 11. Supervised Statistical Techniques
Chapter 12. Unsupervised Clustering Techniques
Chapter 13. Specialized Techniques
Chapter 14. The Data Warehouse

APPENDIX A—SOFTWARE AND DATA SETS FOR DATA MINING
APPENDIX B—STATISTICS FOR PERFORMANCE EVALUATION