Data Mining: A Tutorial-Based Primer, Second Edition
Richard J. Roiger
December 1, 2016 by Chapman and Hall/CRC
Textbook – 487 Pages – 295 B/W Illustrations
ISBN 9781498763974 – CAT# K28997
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
- Provides in-depth coverage of basic and advanced topics in data mining and knowledge discovery
- Presents the most popular data mining algorithms in an easy to follow format
- Includes instructional tutorials on applying the various data mining algorithms
- Provides several interesting datasets ready to be mined
- Offers in-depth coverage of RapidMiner Studio and Weka’s Explorer interface
- Teaches the reader (student,) hands-on, about data mining using RapidMiner Studio and Weka
- Gives instructors a wealth of helpful resources, including all RapidMiner processes used for the tutorials and for solving the end of chapter exercises. Instructors will be able to get off the starting block with minimal effort
- Extra resources include screenshot sequences for all RapidMiner and Weka tutorials and demonstrations, available for students and instructors alike
|February 17, 2017
|February 01, 2017
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“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.