Data Mining: A Tutorial-Based Primer, Second Edition(Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

"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.

Table of Contents:
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 APPROACH DATA MINING, BIG DATA, AND CLOUD COMPUTING DATA MINING ETHICS INTRINSIC VALUE AND CUSTOMER CHURN CHAPTER SUMMARY　 KEY TERMS Data Mining: A Closer Look DATA MINING STRATEGIES SUPERVISED DATA MINING TECHNIQUES ASSOCIATION RULES CLUSTERING TECHNIQUES EVALUATING PERFORMANCE CHAPTER SUMMARY KEY TERMS Basic Data Mining Techniques CHAPTER OBJECTIVES 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 　 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 SET DATA PREPROCESSING DATA TRANSFORMATION DATA MINING INTERPRETATION AND EVALUATION TAKING ACTION THE CRISP-DM PROCESS MODEL CHAPTER SUMMARY KEY TERMS 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 　 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 VIEW CHAPTER SUMMARY KEY 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 DATA UNSUPERVISED NEURAL NET CLUSTERING CHAPTER SUMMARY KEY TERMS 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 　 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 CLUSTERING CHAPTER SUMMARY KEY TERMS 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 The Data Warehouse OPERATIONAL DATABASES DATA WAREHOUSE DESIGN ONLINE ANALYTICAL PROCESSING EXCEL PIVOT TABLES FOR DATA ANALYTICS CHAPTER SUMMARY KEY TERMS