Data Mining

A Tutorial-Based Primer, Second Edition

Richard J. Roiger

• Publisher: Chapman and Hall/CRC
• Year: 2017
• eBook ISBN: 9781498763998
• Topic: Economics
• Subtopic: Statistics for Business & Economics
• Index: Economics

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.

Index

Page numbers followed by f and t indicate figures and tables, respectively.

A

Absolute rarity, 406

Access to data

data warehouse, 20–21

distributed data access, 21

flat files, 21

relational databases, 21

Accuracy

defined, 44

of model, performance evaluation and, 51

of model, technique selection and, 97

Acme credit card database, 202, 203f, 429

Actual customer value

vs. intrinsic value, 26–27, 26f

ACZ. see Attribute and Cluster Analyzer (ACZ)

AdaBoost operator, RapidMiner’s, 414–415, 415f–417f

Adaptive websites, 396

Affinity analysis, 80

Agglomerative clustering, 236, 358–360

applications, 360

credit card promotion database (example), 358–360, 358t, 359t

general considerations, 360

steps of, 358

Aggregate operator, RapidMiner, 185

ANOVA, one-way, 234

ANOVA Matrix operator, 242

ANOVA operator, RapidMiner, 222, 238–240, 239f–241f, 414, 417f

Apple Computer, 379

Apply Model operator, RapidMiner, 161–162, 164f, 167, 168, 172, 178, 244, 297, 345, 384

Apriori association rule algorithm, 47, 82, 127, 181, 405

optimizations of, 405

parameters for, 129f

ArffViewer, Weka, 453

Artificial intelligence, 5

Association rule(s)/association rule learning, 47–48, 96, 105, 393

Apriori algorithm, 47, 82, 127, 181, 405

confidence and support, 80–81

credit card promotion database subset, 82–83, 82t–83t

example, 84–85

general considerations, 84–85

item sets, 82

limitations, 48

for market basket analysis, 4...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Contents
• List of Figures
• List of Tables
• Preface
• Acknowledgments
• Author
• SECTION I Data Mining Fundamentals
• SECTION II Tools for Knowledge Discovery
• SECTION III Building Neural Networks
• SECTION IV Advanced Data Mining Techniques
• APPENDIX A—SOFTWARE AND DATA SETS FOR DATA MINING
• APPENDIX B—STATISTICS FOR PERFORMANCE EVALUATION
• BIBLIOGRAPHY
• INDEX