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.



section I|102 pages

Data Mining Fundamentals

chapter 1|30 pages

Data Mining

A First View

chapter 2|30 pages

Data Mining

A Closer Look

chapter 3|40 pages

Basic Data Mining Techniques

section II|42 pages

Tools for Knowledge Discovery

chapter 5|54 pages

Knowledge Discovery with RapidMiner

chapter 6|22 pages

The Knowledge Discovery Process

chapter 7|30 pages

Formal Evaluation Techniques

section III|64 pages

Building Neural Networks

chapter 8|18 pages

Neural Networks

chapter 9|22 pages

Building Neural Networks with Weka

chapter 10|22 pages

Building Neural Networks with RapidMiner

section IV|136 pages

Advanced Data Mining Techniques

chapter 11|40 pages

Supervised Statistical Techniques

chapter 12|20 pages

Unsupervised Clustering Techniques

chapter 13|46 pages

Specialized Techniques

chapter 14|28 pages

The Data Warehouse