Data Mining : A Tutorial-Based Primer, Second Edition book cover
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Data Mining
A Tutorial-Based Primer, Second Edition




ISBN 9781498763974
Published December 1, 2016 by Chapman and Hall/CRC
487 Pages 295 B/W Illustrations

 
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Book Description

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

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Author(s)

Biography

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.

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Author - Richard J Roiger
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Richard J Roiger

Professor Emeritus, Minnesota State University, Mankato

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Reviews

"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

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