Data Mining A Tutorial-Based Primer, Second Edition

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.

Data Mining Fundamentals Data Mining: A First View 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 APPROACHDATA MINING, BIG DATA, AND CLOUD COMPUTINGDATA MINING ETHICSINTRINSIC VALUE AND CUSTOMER CHURNCHAPTER SUMMARY KEY TERMS Data Mining: A Closer LookDATA MINING STRATEGIESSUPERVISED DATA MINING TECHNIQUES ASSOCIATION RULES CLUSTERING TECHNIQUES EVALUATING PERFORMANCECHAPTER SUMMARY KEY TERMS Basic Data Mining TechniquesCHAPTER OBJECTIVES DECISION TREESA BASIC COVERING RULE ALGORITHM GENERATING ASSOCIATION RULES THE K-MEANS ALGORITHMGENETIC LEARNING CHOOSING A DATA MINING TECHNIQUE CHAPTER SUMMARY KEY TERMS Tools for Knowledge Discovery Weka—An Environment for Knowledge Discovery GETTING STARTED WITH WEKA BUILDING DECISION TREES GENERATING PRODUCTION RULES WITH PART ATTRIBUTE SELECTION AND NEAREST NEIGHBOR CLASSIFICATION ASSOCIATION RULES COST/BENEFIT ANALYSIS UNSUPERVISED CLUSTERING WITH THE K-MEANS ALGORITHM CHAPTER SUMMARY 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 The Knowledge Discovery Process A PROCESS MODEL FOR KNOWLEDGE DISCOVERY GOAL IDENTIFICATION 2016.3 CREATING A TARGET DATA SETDATA PREPROCESSING DATA TRANSFORMATIONDATA MININGINTERPRETATION AND EVALUATIONTAKING ACTION THE CRISP-DM PROCESS MODEL CHAPTER SUMMARY KEY TERMS Formal Evaluation Techniques WHAT SHOULD BE EVALUATED?TOOLS FOR EVALUATIONCOMPUTING 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 Building Neural Networks Neural Networks FEED-FORWARD NEURAL NETWORKS NEURAL NETWORK TRAINING: A CONCEPTUAL VIEW NEURAL NETWORK EXPLANATION GENERAL CONSIDERATIONS NEURAL NETWORK TRAINING: A DETAILED VIEWCHAPTER SUMMARYKEY TERMS 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 DATAUNSUPERVISED NEURAL NET CLUSTERINGCHAPTER SUMMARY KEY TERMS Building Neural Networks with RapidMinerMODELING THE EXCLUSIVE-OR FUNCTION MINING SATELLITE IMAGE DATA PREDICTING CUSTOMER CHURN RAPIDMINER’S SELF-ORGANIZING MAP OPERATOR CHAPTER SUMMARY Advanced Data Mining Techniques Supervised Statistical Techniques BAYES CLASSIFIER SUPPORT VECTOR MACHINES LINEAR REGRESSION ANALYSIS REGRESSION TREES LOGISTIC REGRESSION CHAPTER SUMMARY KEY TERMS Unsupervised Clustering Techniques AGGLOMERATIVE CLUSTERING CONCEPTUAL CLUSTERING EXPECTATION MAXIMIZATION GENETIC ALGORITHMS AND UNSUPERVISED CLUSTERINGCHAPTER SUMMARY KEY TERMS Specialized TechniquesTIME-SERIES ANALYSIS MINING THE WEB MINING TEXTUAL DATA TECHNIQUES FOR LARGE-SIZED, IMBALANCED, AND STREAMING DATAENSEMBLE TECHNIQUES FOR IMPROVING PERFORMANCE CHAPTER SUMMARY KEY TERMS The Data WarehouseOPERATIONAL DATABASES DATA WAREHOUSE DESIGN ONLINE ANALYTICAL PROCESSING EXCEL PIVOT TABLES FOR DATA ANALYTICSCHAPTER SUMMARY KEY TERMS

Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato where he taught and performed research in the Computer & Information Science Department for 27 years. Dr. Roiger’s Ph.D. degree is in Computer & Information Sciences from the University of Minnesota. Dr. Roiger continues to serve as a part-time faculty member teaching courses in data mining, artificial intelligence and research methods. Richard enjoys interacting with his grandchildren, traveling, writing and pursuing his musical talents.