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Data Mining : a Tutorial-Based Primer, Second Edition.

Author: Richard J Roiger
Publisher: Boca Raton : CRC Press, 2017.
Series: Chapman & Hall/CRC data mining and knowledge discovery series.
Edition/Format:   eBook : Document : English : 2nd edView all editions and formats
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Roiger, Richard J.
Data Mining : A Tutorial-Based Primer, Second Edition.
Boca Raton : CRC Press, ©2017
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Richard J Roiger
ISBN: 9781498763981 1498763987
OCLC Number: 1001379779
Notes: 4.2 BUILDING DECISION TREES.
Description: 1 online resource (530 pages).
Contents: Cover; Half Title; Title Page; Copyright Page; Contents; List of Figures; List of Tables; Preface; Acknowledgments; Author; Section I: Data Mining Fundamentals; Chapter 1. Data Mining: A First View; CHAPTER OBJECTIVES; 1.1 DATA SCIENCE, ANALYTICS, MINING, AND KNOWLEDGE DISCOVERY IN DATABASES; 1.1.1 Data Science and Analytics; 1.1.2 Data Mining; 1.1.3 Data Science versus Knowledge Discovery in Databases; 1.2 WHAT CAN COMPUTERS LEARN?; 1.2.1 Three Concept Views; 1.2.1.1 The Classical View; 1.2.1.2 The Probabilistic View; 1.2.1.3 The Exemplar View; 1.2.2 Supervised Learning. 1.2.3 Supervised Learning: A Decision Tree Example1.2.4 Unsupervised Clustering; 1.3 IS DATA MINING APPROPRIATE FOR MY PROBLEM?; 1.3.1 Data Mining or Data Query?; 1.3.2 Data Mining versus Data Query: An Example; 1.4 DATA MINING OR KNOWLEDGE ENGINEERING?; 1.5 A NEAREST NEIGHBOR APPROACH; 1.6 A PROCESS MODEL FOR DATA MINING; 1.6.1 Acquiring Data; 1.6.1.1 The Data Warehouse; 1.6.1.2 Relational Databases and Flat Files; 1.6.1.3 Distributed Data Access; 1.6.2 Data Preprocessing; 1.6.3 Mining the Data; 1.6.4 Interpreting the Results; 1.6.5 Result Application. 1.7 DATA MINING, BIG DATA, AND CLOUD COMPUTING1.7.1 Hadoop; 1.7.2 Cloud Computing; 1.8 DATA MINING ETHICS; 1.9 INTRINSIC VALUE AND CUSTOMER CHURN; 1.10 CHAPTER SUMMARY; 1.11 KEY TERMS; Chapter 2. Data Mining: A Closer Look; CHAPTER OBJECTIVES; 2.1 DATA MINING STRATEGIES; 2.1.1 Classicfiation; 2.1.2 Estimation; 2.1.3 Prediction; 2.1.4 Unsupervised Clustering; 2.1.5 Market Basket Analysis; 2.2 SUPERVISED DATA MINING TECHNIQUES; 2.2.1 The Credit Card Promotion Database; 2.2.2 Rule-Based Techniques; 2.2.3 Neural Networks; 2.2.4 Statistical Regression; 2.3 ASSOCIATION RULES. 2.4 CLUSTERING TECHNIQUES2.5 EVALUATING PERFORMANCE; 2.5.1 Evaluating Supervised Learner Models; 2.5.2 Two-Class Error Analysis; 2.5.3 Evaluating Numeric Output; 2.5.4 Comparing Models by Measuring Lift; 2.5.5 Unsupervised Model Evaluation; 2.6 CHAPTER SUMMARY; 2.7 KEY TERMS; Chapter 3. Basic Data Mining Techniques; CHAPTER OBJECTIVES; 3.1 DECISION TREES; 3.1.1 An Algorithm for Building Decision Trees; 3.1.2 Decision Trees for the Credit Card Promotion Database; 3.1.3 Decision Tree Rules; 3.1.4 Other Methods for Building Decision Trees; 3.1.5 General Considerations. 3.2 A BASIC COVERING RULE ALGORITHM3.3 GENERATING ASSOCIATION RULES; 3.3.1 Confidence and Support; 3.3.2 Mining Association Rules: An Example; 3.3.3 General Considerations; 3.4 THE K-MEANS ALGORITHM; 3.4.1 An Example Using K-means; 3.4.2 General Considerations; 3.5 GENETIC LEARNING; 3.5.1 Genetic Algorithms and Supervised Learning; 3.5.2 General Considerations; 3.6 CHOOSING A DATA MINING TECHNIQUE; 3.7 CHAPTER SUMMARY; 3.8 KEY TERMS; Section II: Tools for Knowledge Discovery; Chapter 4. Weka-An Environment for Knowledge Discovery; CHAPTER OBJECTIVES; 4.1 GETTING STARTED WITH WEKA.
Series Title: Chapman & Hall/CRC data mining and knowledge discovery series.

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