Mining. Practical. Data. Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press. Taylor & Francis Group

Transcription

1 Practical Data Mining Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press Taylor & Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor Ei Francis Group, an Informs business AN AUERBACH BOOK

2 Contents Dedication v Preface xv About the Author xxi Acknowledgments xxiii Chapter 1 What Is Data Mining and What Can It Do? 1 Purpose 1 Goals Introduction A Brief Philosophical Discussion The Most Important Attribute of the Successful Data Miner: Integrity What Does Data Mining Do? What Do We Mean By Data? Nominal Data vs. Numeric Data Discrete Data vs. Continuous Data Coding and Quantization as Inverse Processes A Crucial Distinction: Data and Information Are Not the Same Thing The Parity Problem Five Riddles about Information Seven Riddles about Meaning Data Complexity Computational Complexity 15

3 viii Practical Data Mining Some NP-Hard Problems Some Worst-Case Computational Complexities Summary 17 Chapter 2 The Data Mining Process 19 Purpose 19 Goals Introduction Discovery and Exploitation Eleven Key Principles of Information Driven Data Mining Key Principles Expanded Type of Models: Descriptive, Predictive, Forensic Domain Ontologies as Models Descriptive Models Predictive Models Forensic Models Data Mining Methodologies Conventional System Development: Waterfall Process Data Mining as Rapid Prototyping A Generic Data Mining Process RAD Skill Set Designators Summary 36 Chapter 3 Problem Definition (Step 1) 37 Purpose 37 Goals Introduction Problem Definition Task 1: Characterize Your Problem Problem Definition Checklist Identify Previous Work Data Demographics User Interface Covering Blind Spots Evaluating Domain Expertise Tools Methodology Needs 54

4 Contents ix 3.4 Candidate Solution Checklist What Type of Data Mining Must the System Perform? Multifaceted Problems Demand Multifaceted Solutions The Nature of the Data Problem Definition Task 2: Characterizing Your Solution Candidate Solution Checklist Problem Definition Case Study Predictive Attrition Model: Summary Description Glossary The ATM Concept Operational Functions Predictive Modeling and ATM Cognitive Systems and Predictive Modeling The ATM Hybrid Cognitive Engine Testing and Validation of Cognitive Systems Spiral Development Methodology Summary 70 Chapter 4 Data Evaluation (Step 2) 71 Purpose 71 Goals Introduction Data Accessibility Checklist How Much Data Do You Need? Data Staging Methods Used for Data Evaluation Data Evaluation Case Study: Estimating the Information Content Features Some Simple Data Evaluation Methods Data Quality Checklist Summary 87 Chapter 5 Feature Extraction and Enhancement (Step 3) 89 Purpose 89 Goals Introduction: A Quick Tutorial on Feature Space Data Preparation Guidelines 90

5 x Practical Data Mining General Techniques for Feature Selection and Enhancement Characterizing and Resolving Data Problems Outlier Case Study Winnowing Case Study: Principal Component Analysis for Feature Extraction Principal Component Analysis Feature Winnowing and Dimension Reduction Checklist Checklist for Characterizing and Resolving Data Problems Synthesis of Features Feature Synthesis Case Study Synthesis of Features Checklist Degapping Degapping Case Study Feature Selection Checklist Summary 119 Chapter 6 Prototyping Plan and Model Development (Step 4) 121 Purpose 121 Goals Introduction Step 4A: Prototyping Plan Prototype Planning as Part of a Data Mining Project Prototyping Plan Case Study Step 4B: Prototyping/Model Development Model Development Case Study Summary 141 Chapter 7 Model Evaluation (Step 5) 143 Purpose 143 Goals Introduction Evaluation Goals and Methods Performance Evaluation Components Stability Evaluation Components 144

6 Contents xi 7.3 What Does Accuracy Mean? Confusion Matrix Example Other Metrics Derived from the Confusion Matrix Model Evaluation Case Study: Addressing Queuing Problems by Simulation Model Evaluation Checklist Summary 155 Chapter 8 Implementation (Step 6) 157 Purpose 157 Goals Introduction Implementation Checklist Quantifying the Benefits of Data Mining ROI Case Study ROI Checklist Tutorial on Ensemble Methods Many Predictive Modeling Paradigms Are Available Adaptive Training Getting It Wrong: Mistakes Every Data Miner Has Made Summary 176 Chapter 9 Supervised Learning Genre Section 1 Detecting and Characterizing Known Patterns 179 Purpose 179 Goals Introduction Representative Example of Supervised Learning: Building a Classifier Problem Description Data Description: Background Research/ Planning Descriptive Modeling of Data: Preprocessing and Data Conditioning Data Exploitation: Feature Extraction and Enhancement 185

7 xii Practical Data Mining Model Selection and Development Model Training Model Evaluation Specific Challenges, Problems, and Pitfalls of Supervised Learning High-Dimensional Feature Vectors (PCA, Winnowing) Not Enough Data Too Much Data Unbalanced Data Overtraining Noncommensurable Data: Outliers Missing Features Missing Ground Truth Recommended Data Mining Architectures for Supervised Learning Descriptive Analysis Technical Component: Problem Definition Technical Component: Data Selection and Preparation Technical Component: Data Representation Predictive Modeling Technical Component: Paradigm Selection Technical Component: Model Construction and Validation Technical Component: Model Evaluation (Functional and Performance Metrics) Technical Component: Model Deployment Technical Component: Model Maintenance Summary 204 Chapter 10 Forensic Analysis Genre Section 2 Detecting, Characterizing, and Exploiting Hidden Patterns 205 Purpose 205 Goals Introduction Genre Overview Recommended Data Mining Architectures for Unsupervised Learning 207

8 Contents xiii 10.4 Examples and Case Studies for Unsupervised Learning Case Study: Reducing Cost by Optimizing a System Configuration Case Study: Stacking Multiple Pattern Processors for Broad Functionality Multiparadigm Engine for Cognitive Intrusion Detection Tutorial on Neural Networks The Neural Analogy Artificial Neurons: Their Form and Function Using Neural Networks to Learn Complex Patterns Making Syntactic Methods Smarter: The Search Engine Problem A Submetric for Sensitivity A Submetric for Specificity Combining the Submetrics to Obtain a Single Score Putting It All Together: Building a Simple Search Engine The Objective Function for This Search Engine and How to Use It Summary 231 Chapter 11 Purpose Genre Section 3 Knowledge: Its Acquisition, Representation, and Use Goals Introduction to Knowledge Engineering The Prototypical Example: Knowledge-Based Expert Systems (KBES) Inference Engines Implement Inferencing Strategies Computing with Knowledge Graph Methods: Decision Trees, Forward/ Backward Chaining, Belief Nets Bayesian Belief Networks Non-Graph Methods: Belief Accumulation Inferring Knowledge from Data: Machine Learning Learning Machines 247

9 xiv Practical Data Mining Using Modeling Techniques to Infer Knowledge from History Domain Knowledge the Learner Will Use Inferring Domain Knowledge from Human Experts Writing on a Blank Slate Mathematizing Human Reasoning Using Facts in Rules Problems and Properties Summary 259 References 261 Glossary 263 Index 269