Handbook of statistical analysis and data mining applications:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London ; San Diego, CA ; Cambridge, MA ; Oxford
Academic Press, an imprint of Elsevier
[2018]
|
| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxix, 792 Seiten Illustrationen, Diagramme |
| ISBN: | 9780124166325 |
Internformat
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| 245 | 1 | 0 | |a Handbook of statistical analysis and data mining applications |c authors: Robert Nisbet, Gary Miner, Ken Yale ; guest editors of selected chapters: John Elder IV, Andy Peterson |
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Datensatz im Suchindex
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Contents 2. Theoretical Considerations for Data Mining List of Tutorials on the Elsevier Companion Web Page xi Foreword 1 for 1st Edition xiii Foreword 2 for 1st Edition xv Preface xvii Introduction xxi Frontispiece xxv Biographies of the Primary Authors of This Book xxvii Preamble .21 í The Scientific Method 21 What Is Data Mining? 22 A Theoretical Framework for the Data Mining Process '24 Strengths of the Data Mining Process 25 Customer-Centric Versus Account-Centric: A New Way to Look at Your Data 25 The Data Paradigm Shift 27 Creation of the Car 28 Major Activities of Data Mining 28 Major Challenges of Data Mining 30 General Examples of Data Mining Applications- 31 Major Issues in Data Mining 31 General Requirements for Success in a Data Mining Project 33 Example of a Data Mining Project: Classify a Bat’s Species by Its Sound 33 The Importance of Domain Knowledge 35 Postscript 35 References 36 Further Reading 37 I HISTORY OF PHASES OF DATA ANALYSIS, BASIC THEORY, AND THE DATA MINING PROCESS 1. The Background for Data Mining . - Practice Preamble 3 Data Mining or Predictive Analytics? 4 A Short History of Statistics and Predictive Analytics 6 Modem Statistics: A Duality? 6 Two Views of Reality 11 The Rise of Modem Statistical Analysis: The Second Generation 13 Machine Learning Methods: The Third Generation 15 Statistical Learning Theory: The Fourth Generation 16 Reinforced and Deep Learning 18 Current Trends of Development in Predictive Analytics 18 Postscript 19 References 19 3. The Data Mining and Predictive Analytic Process Preamble 39 The Science of Data
Mining/Predictive Analytics 39 The Approach to Understanding and Problem Solving 40 CRISP-DM 40 Business Understanding (Mostly Art) 42 Data Understanding (Mostly Science) 44 Data Preparation (A Mixture of Art and Science) 47 Modeling (A Mixture of Art and Science) 47 Deployment (Mostly Art) 52 , , . V
VI CONTENTS Closing the Information Loop (Art) The Art of Data Mining 52 Postscript 53 References 54 52 4. Data Understanding and Preparation Preamble 55 Activities of Data Understanding and Preparation 55 Issues That Should Be Resolved 56 Data Understanding 57 Postscript 81 References 82 Further Reading 82 / / 8. Advanced Algorithms for Data Mining ՝ 5. Feature Selection Preamble 83 , Variables as Features 83 Types of Feature Selection .84 Feature Ranking Methods 84 Subset Selection Methods 88 Postscript 97 : References 97 Generalized Additive Models (GAM) 136 Classification and Regression Trees (CART) 138 Generalized EM and k-Means Cluster Analysis—An Overview 145 Postscript 147 References 147 Further Reading 147 1 6. Accessory Tools for Doing Data Mining Preamble 99 Data Access Tools. 100 , J' Data Exploration Tools 102 Modeling Management Tools 108 Modeling Analysis Tools 110 In-place Data Processing (IDP) 113 Rapid Deployment of Predictive Models 115 Model Monitors 117 Postscript 117 Further Reading 117 Preamble 149 Introduction 150 · Advanced Data Mining Algorithms 151 Quality Control Data Mining and Root Cause Analysis 166 Postscript 167 References 167 Further Reading 167 9. Classification Preamble 169 What Is Classification? 169 Initial Operations in Classification 169 Major Issues With Classification 170 Assumptions of Classification Procedures 171 Analyzing Imbalanced Data Sets With Machine . Learning Programs 172 Phases in the Operation of Classification Algorithms 172 Advantages and Disadvantages of Common Classification Algorithms 174 CHAID 177 Which Algorithm
Is Best for Classification? 185 Postscript 186 References 186 Further Reading 186 10. Numerical Prediction II THE ALGORITHMS AND METHODS IN DATA MINING AND PREDICTIVE ANALYTICS AND SOME DOMAIN AREAS 7. Basic Algorithms for Data Mining: A Brief Overview Preamble 121 Introduction 121 Preamble 187 Linear Response Analysis and the Assumptions of the Parametric Model 188 ^ Parametric Statistical Analysis 188 Assumptions of the Parametric Model 189 Linear Regression 192 · Generalized Linear Model (GLM) 195 Methods for Analyzing Nonlinear Relationships 198 Nonlinear Regression and Estimation 198 Data Mining and Machine Learning Algorithms Used in Numerical Prediction 201 ՛ Advantages of Classification and Regression Trees (CART) Methods 205 ՝
vii CONTENTS Application to Mixed Models 207 Neural Nets for Prediction 208 Support Vector Machines (SVMS) and Other Kernel Learning Algorithms 211 Postscript 212 References 213 11. Model Evaluation and Enhancement Preamble 215 Evaluation and Enhancement: Part of the Modeling Process 215 Types of Errors in Analytical Models 216 ՝ · Model Enhancement Techniques 227 Model Enhancement Checklist 231 Postscript 232 References 232 12. Predictive Analytics for Population Health and Care Preamble 235 The Future of Healthcare, and How Predictive Analytics Fits 235 Predictive Analytics and Population Health 246 Predictive Analytics and Precision Medicine 253 Postscript 257 References 257 . Further Reading 258 13. Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors ANDY PETERSON Postscript 288 References 288 15. Fraud Detection Preamble 289 Issues With Fraud Detection 289 How Do You Detect Fraud? 292 Supervised Classification of Fraud 293 How Do You Model Fraud? 294 How Are Fraud Detection Systems Built? 295 Intrusion Detection Modeling 296 Comparison of Models With and Without Time-Based Features 297 Building Profiles 301 Deployment of Fraud Profiles 302 Postscript 302 References 302 III TUTORIALS AND CASE STUDIES Tutorial A Example of Data Mining Recipes Using Windows 10 and Statistica 13 LINDA A. MINER Tutorial В Using the Statistica Data Mining Workspace Method for Analysis of Hurricane Data (Hurrdata.sta) JEFF WONG Preamble 259 Introduction 259 Industrial Integration of Educational Psychology and Big Data Analytics 274
Postscript 275 References 276 Further Reading 277 14. Customer Response Modeling Preamble 279 Early CRM Issues in Business 279 Knowing How Customers Behaved Before They Acted 280 CRM in Business Ecosystems 281 Conclusions 287 Tutorial C Case Study—Using SPSS Modeler and STATISTICA to Predict Student Success at High-Stakes Nursing Examinations (NCLEX) GALINA BELOKUROVA, CHIARINA PIAZZA Introduction 335 ՝ Decision Management in Nursing Education 336 ՛ Case Study 337 Research Question 337 Literature Review 337 Dataset and Expected Strength of Predictors 338 Data Mining With SPSS Modeler 339
viii * CONTENTS ' Data Mining With STATISTICA Conclusion 355 References 356 Further Reading 357 348 Tutorial D Constructing a Histogram in KNIME Using MidWest Company Personality Data Tutorial I Ч Data Prep 1-2: Data Description ROBERTA BORTOLOTTI, MSIS, СВАР Tutorial J Data Prep 2-1: Data Cleaning and Recoding Roberta bortolotti LINDA A. MINER Tutorial E Feature Selection in KNIME BOBNISBET ROBERTA BORTOLOTTI Why Select Features? 377 Occam’s Razor—Simple, But Not Simplistic 377 Local Minimum Error 378 Moving Out of the Local Minimum 379 Strategies for Reduction of Dimensionality in Predictive Analytics Available in KNIME 379 Tutorial F Tutorial К Data Prep 2-2: Dummy Coding Category Variables Medical/Business Tutorial Tutorial L Data Prep 2-3: Outlier Handling ROBERTA BORTOLOTTI Tutorial M Data Prep 3-1: Filling Missing Values With Constants ROBERTA BORTOLOTTI LINDA A. MINER Tutorial G A KNIME Exercise, Using Alzheimer’s Training Data of Tutorial F Tutorial N Data Prep 3-2: Filling Missing Values With Formulas ROBERTA BORTOLOTTI i LINDA A. MINER Introduction 423 KNIME Project 423 Getting the Program to Open Microsoft Excel CSV File: Alzheimer Training Data 426 Decision Trees Node 428 Linear Correlation Node 432 Conditional Box Plot Node 436 Decision Trees Again 438 End Note 442 Tutorial О Data Prep 3-3: Filling Missing Values With a Model ROBERTA BORTOLOTTI Tutorial P City of Chicago Crime Map: A Case Study Predicting Certain Kinds of Crime Using Statistica Data Miner and Text Miner ENDRINTUSHE Tutorial H Data Prep 1-1: Merging Data Sources ROBERTA BORTOLOTTI, MSIS, СВАР
Data Analysis 599 Text Mining 606 Boosted Trees 614
ix CONTENTS Tutorial Q Using Customer Chum Data to Develop and Select a Best Predictive Model for Client Defection Using STATISTICA,Data Miner 13 64-bit for Windows 10 RICHARD PORTER WITH ASSISTANCE OF ROBERT NISBET, LINDA A. MINER, GARY MINER About This Tutorial 627 Business Objectives 627 Data Preparation ' 630 Feature Selection 642 Building a Predictive Model With STATISTICA Data Miner DMRecipes 646 Model Evaluation 648 Tutorial R Example With C RT to Predict and Display Possible Structural Relationships GREG ROBINSON, LINDA A. MINER, MARY A. MILLIKIN References 674 Tutorial S Clinical Psychology: Making Decisions About Best Therapy for a Client LINDA A. MINER _ IV_ MODEL ENSEMBLES, MODEL COMPLEXITY; USING THE RIGHT MODEL FOR THE RIGHT USE, SIGNIFICANCE, ETHICS, AND THE FUTURE, AND ADVANCED PROCESSES 16. The Apparent Paradox of Complexity in Ensemble Modeling JOHN ELDER, ANDY PETERSON Preamble 705 Introduction 706 Model Ensembles 706 How Measure Model Complexity? 709 Generalized Degrees of Freedom 711 Examples: Decision Tree Surface With Noise Summary and Discussion 715 Postscript 716 Acknowledgment 717 References 717 Further Reading 718 712 17. The “Right Model” for the “Right Purpose”: When Less Is Good Enough Preamble 719 More Is Not Necessarily Better: Lessons From Nature and Engineering 720 Postscript 726 References 726 18. A Data Preparation Cookbook Preamble 727 ¡ Introduction 727 CRISP-DM—Business Understanding Phase 728 CRISP-DM—Data Understanding Phase 729 CRISP-DM—Data Preparation Phase 732 CRISP-DM—Modeling Phase 736 18 Common Mistakes in Data
Preparation in Predictive Analytics Projects 736 Postscript 739 References 740 19. Deep Learning Preamble 741 The Guiding Concept of DL Technology—Human Cognition 742 Early Artificial Neural Networks (ANNs) 743 How ANNs Work 745 More Elaborate Architectures—DL Neural Networks 746 Postscript 750 References 751 Further Reading 751 20. Significance versus Luck in the. Age of Mining: The Issues of P-Value “Significance” and “Ways to Test Significance of Our Predictive Analytic Models” Preamble 753 Introduction 753
x CONTENTS The Problem of Significance in Traditional P-Value Statistical Analysis 754 USUAL Data Mining/Predictive Analytic Performance Measures—-Terminology 759 Unique Ways to Test Accuracy (“Significance”) of Machine Learning Predictive Models 760 Compare Predictive Model Performance Against Random Results With Lift Charts and Decile Tables 760 Evaluate the Validity of Your Discovery With Target Shuffling 762 Test Predictive Model Consistency With Bootstrap Sampling 763 Postscript 764 References 765 21. Ethics and Data Analytics ANDY PETERSON Preamble 767 The Birthday Party—A Practical Example for Ethical Action 767 Academic Secular Ethics 768 Ethics and Data Science for the Norms of· Government (Deontological-Normative) 769 Ethics and Data Science for the Goals in Business (Situational-Teleological) 769 Ethics and Data Science for the Virtues of Personal Life (Existential-Motivational) 769 Combination: Right Standards, Right Goals, and Personal Virtue (Normative, Situational, Existential) 770 Michael Sandel on “Doing The Right Thing” With Data Analytics 770 Discovering Data Ethics in an “Alignment Methodology” 771 References 772 Further Reading 772 V : · . ' 22. IBM Watson Preamble 773 Introduction 773 What Exactly Is Watson ľ 773 Jeopardy! 774 Internal Features of Watson 774 Application Programming Interfaces (APIs) Software Development Kits (SDKs) 778 Some Existing Applications of Watson Techology 778 Ushering in the Cognitive Era 780 Postscript 780 Reference 781 Index 783 776 |
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| spelling | Nisbet, Robert 1942- Verfasser (DE-588)140490930 aut Handbook of statistical analysis and data mining applications authors: Robert Nisbet, Gary Miner, Ken Yale ; guest editors of selected chapters: John Elder IV, Andy Peterson Second edition London ; San Diego, CA ; Cambridge, MA ; Oxford Academic Press, an imprint of Elsevier [2018] © 2018 xxix, 792 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Data Mining (DE-588)4428654-5 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Data Mining (DE-588)4428654-5 s Statistik (DE-588)4056995-0 s b DE-604 Miner, Gary 1942- Verfasser (DE-588)140491813 aut Yale, Ken 1956- Verfasser (DE-588)1156297931 aut Erscheint auch als Online-Ausgabe 978-0-12-416645-5 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030104075&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Handbook of statistical analysis and data mining applications |
| title_auth | Handbook of statistical analysis and data mining applications |
| title_exact_search | Handbook of statistical analysis and data mining applications |
| title_full | Handbook of statistical analysis and data mining applications authors: Robert Nisbet, Gary Miner, Ken Yale ; guest editors of selected chapters: John Elder IV, Andy Peterson |
| title_fullStr | Handbook of statistical analysis and data mining applications authors: Robert Nisbet, Gary Miner, Ken Yale ; guest editors of selected chapters: John Elder IV, Andy Peterson |
| title_full_unstemmed | Handbook of statistical analysis and data mining applications authors: Robert Nisbet, Gary Miner, Ken Yale ; guest editors of selected chapters: John Elder IV, Andy Peterson |
| title_short | Handbook of statistical analysis and data mining applications |
| title_sort | handbook of statistical analysis and data mining applications |
| topic | Data Mining (DE-588)4428654-5 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Data Mining Statistik |
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