Includes bibliographical references and index. Roiger, R. J. (2017). Data mining: A tutorial-based primer (2nd ed.). Boca Raton, FL: CRC Press.

Section I: Data mining Fundamentals -- Data Science, Analytics, Mining, and Knowledge Discovery in Databases -- What Can Computers Learn? -- Is Data Mining Appropriate for My Problem? -- Data Mining or Knowledge Engineering? -- A Nearest Neighbor Approach -- A Process Model for Data Mining -- Data Mining, Big Data, and Cloud Computing -- Data Mining Ethics -- Intrinsic Value and Customer Churn -- Chapter Summary -- Key Terms -- Chapter 2: Data Mining: A Closer Look -- Data Mining Strategies -- Supervised Data Mining Techniques -- Association Rules -- Clustering Techniques -- Evaluating Performance -- Chapter Summary -- Key Terms -- Chapter 3: Basic Data Mining Techniques -- Decision Trees -- A Basic Covering Rule Algorithm -- Generating Association Rules -- The K-Means Algorithm -- Genetic Learning -- Choosing a Data Mining Technique -- Chapter Summary -- Key Terms -- Section II: Tools for Knowledge Discovery -- Chapter 4: Weka-An Environment for Knowledge Discovery -- Getting Started with Weka -- Building Decision Trees -- Generating Production Rules with Part -- Attribute Selection and Nearest Neigbhor Classification -- Association Rules -- Cost/Benefit Analysis, (Optional) -- Unsupervised Clustering with the K-Means Algorithm -- Chapter Summary -- Chapter 5: Knowledge Discovery with RapidMiner -- Getting Started with Rapidminer -- Building Decision Trees -- Generating Rules -- Association Rule Learning -- Unsupervised Clustering with K-Means -- Attribute Selection and Nearest Neighbor Classification -- Chapter Summary -- Chapter 6: The Knowledge Discovery Process -- A Process Model for Knowledge Discovery -- Goal Identification -- Creating a Target Data Set -- Data Preprocessing -- Data Transformation -- Data Mining -- Interpretation and Evaluation -- Taking Action -- The Crisp-DM Process Model -- Chapter Summary -- Key Terms -- Chapter 7: Formal Evaluation Techniques -- What Should Be Evaluated? -- Tools for Evaluation -- Computing Test Set Confidence Intervals -- Comparing Supervised Learner Models -- Unsupervised Evaluation Techniques -- Evaluating Supervised Models with Numeric Output -- Comparing Models with Rapidminer -- Attribute Evaluation for Mixed Data Types -- Pareto Lift Charts -- Chapter Summary -- Key Terms -- Section III: Building Neural Networks -- Chapter 8: Neural Networks -- Feed-Forward Neural Networks -- Neural Network Training: A Conceptual View -- Neural Network Explanation -- General Considerations -- Neural Network Training: A Detailed View -- Chapter Summary -- Key Terms -- Chapter 9: Building Neural Networks with Weka -- Data Sets for Backpropagation Learning -- Modeling the Exclusive-or Function: Numeric Output -- Modeling the Exclusive-or Function: Categorical Output -- Mining Satellite Image Data -- Unsupervised Neural Net Clustering -- Chapter Summary -- Key Terms -- Chapter 10: Building Neural Networks with Rapidminer -- Modeling the Exclusive-or Function -- Mining Satellite Image Data -- Predicting Customer Churn -- Rapidminer's Self-Organizing Map Operator -- Chapter Summary -- Section IV: Advanced Data Mining Techniques -- Chapter 11: Supervised Statistical Techniques -- Naive Bayes Classifier -- Support Vector Machines -- Linear Regression Analysis -- Regression Trees -- Logistic Regression -- Chapter Summary -- Key Terms -- Chapter 12: Unsupervised Clustering Techniques -- Agglomerative Clustering -- Conceptual Clustering -- Expectation Maximization -- Genetic Algorithms and Unsupervised Clustering -- Chapter Summary -- Key Terms -- Chapter 13: Specialized Techniques -- Time-Series Analysis -- Mining the Web -- Mining Textual Data -- Techniques for Large-Sized, Imbalanced, and Streaming Data -- Ensemble Techniques for Improving Performance -- Chapter Summary -- Key Terms -- Chapter 14: The Data Warehouse -- Operational Databases -- Data Warehouse Design -- Online Analytical Processing -- Excel Pivot Tables for Data Analytics -- Chapter Summary -- Key Terms

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.