Roiger R.J. Data Mining: A Tutorial-Based Primer

2nd ed. — Taylor & Francis;Chapman and Hall/CRC, 2017. — 530 p. — (Chapman & Hall/CRC data mining and knowledge discovery). — ISBN: 9781498763974, 1051051061, 1498763979, 9781498763981, 1498763987Data 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.Data Mining Fundamentals
Data Mining: A First View
Data Mining: A Closer Look
Basic Data Mining Techniques
Tools for Knowledge Discovery
Weka — An Environment for Knowledge Discovery
Knowledge Discovery with RapidMiner
The Knowledge Discovery Process
Formal Evaluation Techniques
Building Neural Networks
Neural Networks
Building Neural Networks with Weka
Building Neural Networks with RapidMiner
Advanced Data Mining Techniques
Supervised Statistical Techniques
Unsupervised Clustering Techniques
Specialized Techniques
The Data Warehouse
Appendix A. Software and Data Sets for Data Mining
Appendix B. Statistics for Performance Evaluation