Verfügbarkeit: Handbook of statistical analysis and data mining applications

Handbook of statistical analysis and data mining applications:

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Bibliographische Detailangaben
Hauptverfasser:	Nisbet, Robert 1942- (VerfasserIn), Elder, John F. 1961- (VerfasserIn), Miner, Gary 1942- (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Amsterdam [u.a.] Elsevier AP 2009
Schlagworte:	Exploration de données (Informatique) - Méthodes statistiques Statistik Data Mining
Online-Zugang:	Inhaltsverzeichnis
Beschreibung:	XXXIV, 824 S. Ill., graph. Darst. DVD (12 cm)
ISBN:	9780123747655 0123747651

Internformat

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Datensatz im Suchindex

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adam_text	Table of Contents Foreword 1 жу Foreword 2 xvii Preface xix Łitrodttetion xxiii List of Tutorials by Guest Autàors xxix DATA ANALYSIS» BASIC THEORY, AND THE DATA MINING PROCESS 1. The Background fot EMa Mtotog Practice Preamble 3 Ä Short History of Statistics end Data Mining 4 Mildem Statistics: A Duality! 5 Assumptions of the Parametric Model 6 Two Views of Reality 8 Aristotle 8 Plato 9 The Rise of Modem Statistical Analysis! The Second Generation 10 Data, Data Everywhere ... і І Machine Learning Methods; The Third Oeaeiation 11 Statistical Learning Theory; The fourth Generation К Postscript 13 2. Theoretical Considerations for Data Mining Preamble 15 The Sdentine Method 16 What Ь Data Mtaieg? 17 A Theoretical Framework for the Data Mining Process 18 Місюеадпотіе Approach 19 Inductive Database Approach 19 Strengths of the Data Mining Process 19 Customer-Centric Versus Account-Centric: Ä New Way to Look at Your Data 20 The Physical Data Mart 20 The Virtual Data Mart 21 Householded Databases 21 The Data Paradigm Shift 22 Creation of the Car 22 Major Activities of Dam Mining 2,3 Major Challenges of Data Mining 25 Examples of Data Mining Applications 26 Major Issues in Data Mining 26 General Requirements for Success in a Data Mining Project 28 Example of a Data Mining Project: Classify a Bat s Species by Its Sound 28 The Importance of Domain iuiowíedge 30 Postscript 30 Why Did Data Mining Arise? 30 Some Caveats with Data Mining Solutions 31 1 The Data Mining Process Preamble 33 The Science of Data Mining 33 The Approach to Unaerstandïag and Problem Solving 34 CRÏSP-DM 35 Business Understanding {Mostly Art) 36 Define the Business Objectives of the Data Mining Model 36 Assess the Business Environment for Data Mining 37 Pomulase the Dam Mining Goals and Objectives 37 VI TABLE OF CONTENTS Data Understanding (Mostly Science) 39 Data Acquisition 39 Data Integration 39 Data Description 40 Data Quality Assessment 40 Data Preparation (A Mixture of Art and Science) 40 Modeling (A Mixture of Art and Science) 41 Steps in the Modeling Phase of CRISP-DM 41 Deployment (Mostly Art) 45 Closing the Information Loop (Art) Ą6 The Art of Data Mining 46 Artistic Steps in Data Mining 47 Postscript 47 4. Data Understanding and Preparation Preamble 49 Activities of Data Understanding and Preparation 50 Definitions 50 Issues That Should be Resolved 51 Basic Issues That Must Be Resolved in Data Understanding 51 Basic Issues That Must Be Resolved in Data Preparation 51 Data Understanding 51 Data Acquisition 51 Data Extraction 53 Data Description 54 Data Assessment 56 Data Profiling 56 Data Cleansing 56 Data Transformation 57 Data Imputation 59 Data Weighting and Balancing 62 Data Filtering and Smoothing 64 Data Abstraction 66 Data Reduction 69 Data Sampling 69 Data Discretization 73 Data Derivation 73 Postscript 75 5. Feature Selection Preamble 77 Variables as Features 78 Types of Feature Selections 78 Feature Ranking Methods 78 Gini Index 78 Bi-variate Methods 80 Mukivariate Methods 80 Complex Methods 82 Subset Selection Methods 82 The Other Two Ways of Using Feature Selection in STATISTICA: Interactive Workspace 93 $ΤΑΉ$ΤΙ0Α DMRecipe Method 93 Postscript 96 6. Accessory Took for Doing Data Mining Preamble 99 Data Access Tools 100 Structured Query Language (SQL) Tools 1ÖÖ Extract, Transform, and Load (ETL) Capabilities 100 Data Exploration Tools 101 Basic Descriptive Statistics 101 Combining Groups (Classes) for Predictive Data Mining 105 Slicing/Dicing and Drilling Down into Data Sets/ Results Spreadsheets 106 Modeling Management Tools 107 Data Miner Workspace Templates 107 Modeling Analysis Tools 107 Feature Selection 107 Importance Plots of Variables 108 In-Place Data Processing (IDP) 113 Example: The IDP Facility of STATÌSTICA Data Miner 114 How to Use the SQL 114 Rapid Deployment of Predictive Models 114 Model Monitors 116 Postscript 117 TABLE OF CONTENTS Vil THE ALGORITHMS IN DATA MINING AND TEXT MINING, THE ORGANIZATION OF THE THREE MOST COMMON DATA MINING TOOLS, AND SELECTED SPECIALIZED AREAS USING DATA MINING ?. Basic Algorithms for Data Mining: A Brief Overview Preamble 121 STATISTICA Data Miner Recipe (DMRecipe) 123 KXEN 124 Basic Data Mining Algorithms 126 Association Rules 126 Neural Networks 128 Radial Basis Function (RBF) Networks 136 Automated Neural Nets 138 Generalized Additive Models (GAMs) 138 Outputs of GAMs 139 Interpreting Results of GAMs 139 Classification and Regression Trees (CART) 139 Recursive Partitioning 144 Pruning Trees 144 General Comments about CART for Statisticians 144 Advantages of CART over Other Decision Trees 145 Uses of CART 146 General CHAID Models 146 Advantages of CHAID 147 Disadvantages of CHAID 147 Generalized EM and fe-Means Cluster Analysis — An Overview 147 k Means Clustering 147 EM Cluster Analysis 148 Processing Steps of the EM Algorithm 149 V-fold Cross Validation as Applied to Clustering 149 Postscript 150 8. Advanced Algorithms for Data Mining Preample 151 Advanced Data Mining Algorithms 154 Interactive Trees 154 Multivariate Adaptive Regression Splines (MARSplines) 158 Statistical Learning Theory: Support Vector Machines 162 Sequence, Association, and Link Analyses 164 Independent Components Analysis (ICA) 168 Kohonen Networks 169 Characteristics of a Kohonen Network 169 Quality Control Data Mining and Root Cause Analysis 169 Image and Object Data Mining: Visualization and ЗD-Medical and Other Scanning Imaging 170 Postscript 171 s?. Text Mining and Natural Language Processing Preamble 173 The Development of Text Mining 174 A Practical Example: NTSB 175 Goals of Text Mining of NTSB Accident Reports 184 Drilling into Words of Interest 188 Means with Error Plots 189 Feature Selection Tool 190 A Conclusion: Losing Control of the Aircraft in Bad Weather Is Often Fatal 191 Summary 194 Text Mining Concepts Used in Conducting Text Mining Studies 194 Postscript 194 10. The Three Most Common Data Mining Software Tools Preamble 197 SPSS Clementine Overview 197 Overall Organization of Clementine Components 198 Organization of the Clementine Interface 199 Clementine Interface Overview 199 Setting the Default Directory 201 SuperNodes 201 VIU TABLE OF CONTENTS Execution of Streams 202 SAS-Enterprise Miner (SAS -ЕМ) Overview 203 Overall Organization of SAS -ЕМ Version 5.3 Components 203 Layout of the SAS-Enterprise Miner Window 204 Various SAS EM Menus, Dialogs, and Windows Useful During the Data Mining Process 205 Software Requirements to Run SAS-EM 5.3 Software 206 STATISTICA Data Miner, QC-Mmer, and Text Miner Overview 214 Overall Organization and Use of STATISTICA Data Miner 214 Three Formats for Doing Data Mining in STATISTICA 230 Postscript 234 11. Classification Preample 235 What Is Classification? 235 Initial Operations in Classification 236 Major Issues with Classification 236 What Is the Nature of Data Set to Be Classified? 236 How Accurate Does the Classification Have to Be? 236 How Understandable Do the Classes Have to Be? 237 Assumptions of Classification Procedures 237 Numerical Variables Operate Best 237 No Missing Values 237 Variables Are Linear and Independent in Their Effects on the Target Variable 237 Methods for Classification 238 Nearest-Neighbor Classifiers 239 Analyzing Imbalanced Data Sets with Machine Learning Programs 240 CHAID 246 Random Forests and Boosted Trees 248 Logistic Regression 250 Neural Networks 251 Naïve Bayesian Classifiers 253 What Is the Best Algorithm for Classification? 256 Postscript 257 12, Numerical Prediction Preamble 259 Linear Response Analysis and the Assumptions of the Parametric Model 260 Parametric Statistical Analysis 261 Assumptions of the Parametric Model 262 The Assumption of Independency 262 The Assumption of Normality 262 Normality and the Central Limit Theorem 263 The Assumption of Linearity 264 Linear Regression 264 Methods for Handling Variable Interactions in Linear Regression 265 Collinearity among Variables in a Linear Regression 265 The Concept of the Response Surface 266 Generalized Linear Models (GLMs) 270 Methods for Analyzing Nonlinear Relationships 271 Nonlinear Regression and Estimation 271 Logit and Probit Regression 272 Poisson Regression 272 Exponential Distributions 272 Piecewise Linear Regression 273 Data Mining and Machine Learning Algorithms Used in Numerical Prediction 274 Numerical Prediction with C&RT 274 Model Results Available in C&RT 276 Advantages of Classification and Regression Trees (C&RT) Methods 277 General Issues Related to C&RT 279 Application to Mixed Models 280 Neural Nets for Prediction 280 Manual or Automated Operation? 280 Structuring the Network for Manual Operation 280 Modern Neural Nets Are Gray Boxes 281 Example of Automated Neural Net Results 281 Support Vector Machines (SVMs) and Other Kernel Learning Algorithms 282 Postscript 284 ! 3. Model Evaluation and Enhancement Preamble 285 Introduction 286 Model Evaluation 286 Splitting Data 287 TABLE OF CONTENTS ЇХ Avoiding Ovetfit Through Complexity Régularisation 288 Error Metric: Estimation 291 Error Metric: Classification 291 Error Metric: Ranking 293 Cross-Validation to Estimate Error Rate and Its Confidence 295 Bootstrap 296 Target Shuffling to Estimate Baseline Performance 297 Re-Cap of the Most Popular Algorithms 300 Linear Methods (Consensus Method, Stepwise Is Variable-Selecting) 300 Decision Trees (Consensus Method, Variable- Selecting) 300 Neural Networks (Consensus Method) 301 Nearest Neighbors (Contributory Method) 301 Clustering (Consensus or Contributory Method) 302 Enhancement Action Checklist 302 Ensembles of Models: The Single Greatest Enhancement Technique 304 Bagging 305 Boosting 305 Ensembles in General 306 How to Thrive as a Data Miner 307 Big Picture of the Project 307 Project Methodology and Deliverables 308 Professional Development 309 Three Goals 310 Postscript 311 14. Medical Informatics Preamble 313 What Is Medical Informatics? 313 How Data Mining and Text Mining Relate to Medical Informatics 314 XplorMed 316 ABView: HivResist 317 3D Medical Informatics 317 What Is 3D Informatics? 317 Future and Challenges of 3D Medical Informatics 318 Journals and Associations in the Field of Medical Informatics 318 Postscript 318 1 5, Bioinfbrmatics Preamble 321 What Is Bioinformatics? 323 Data Analysis Methods in Bioinformatics 326 ClustalW2: Sequence Alignment 326 Searching Databases for RNA Molecules 327 Web Services in Bioinformatics 327 How Do We Apply Data Mining Methods to Bioinformatics? 329 Postscript 332 Tutorial Associated with This Chapter on Bioinformatics 332 Books, Associations, and Journals on Bioinformatics, and Other Resources, Including Online 332 16. Customer Response Modeling Preamble 335 Early CRM Issues in Business 336 Knowing How Customers Behaved Before They Acted 336 Transforming Corporations into Business Ecosystems: The Path to Customer Fulfillment 337 CRM in Business Ecosystems 338 Differences Between Static Measures and Evolutionary Measures 338 How Can Human Nature as Viewed Through Plato Help Us in Modeling Customer Response? 339 How Can We Reorganize Our Data to Reflect Motives and Attitudes? 339 What Is a Temporal Abstraction? 340 Conclusions 344 Postscript 345 1?. Fraud Detection Preamble 347 Issues with Fraud Detection 348 Fraud Is Rare 348 Fraud Is Evolving! 348 Large Data Sets Are Needed 348 The Fact of Fraud Is Not Always Known during Modeling 348 When the Fraud Happened Is Very Important to Its Detection 349 χ TABLE OF CONTENTS Fraud Is Very Complex 349 Fraud Detection May Require the Formulation of Rules Based on General Principles/ Red Flags, Alerts, and Profiles 349 Fraud Detection Requires Both Internal and External Business Data 349 Very Few Data Sets and Modeling Details Are Available 350 How Do You Detect Fraud? 350 Supervised Classification of Fraud 351 How Do You Model Fraud? 352 How Are Fraud Detection Systems Built? 353 Intrusion Detection Modeling 355 Comparison of Models with and without Time-Based Features 355 Building Profiles 360 Deployment of Fraud Profiles 360 Postscript and Prolegomenon 361 TUTORIALS—STEP-BY-STEP CASE STUDIES AS A STARTING POINT TO LEARN HOW TO DO DATA MINING ANALYSES Guest Authors of the Tutorials A. How to Use Data Miner Recipe What is STATISTICA Data Miner Recipe (DMR)? 373 Core Analytic Ingredients 373 B. Data Mining for Aviation Safety Airline Safety 378 SDR Database 379 Preparing the Data for Our Tutorial 382 Data Mining Approach 383 Data Mining Algorithm. Error Rate 386 Conclusion 387 С Predicting Movie Box-Office Receipts Introduction 391 Data and Variable Definitions 392 Getting to Know the Workspace of the Clementine Data Mining Toolkit 393 Results 396 Publishing and Reuse of Models and Other Outputs 404 D, Detecting Unsatisfied Customers: A Case Study Introduction 418 The Data 418 The Objectives of the Study 418 SAS-EM 5.3 Interface 419 A Primer of SAS -ЕМ Predictive Modeling 420 Homework 1 430 Discussions 431 Homework 2 431 Homework 3 431 Scoring Process and the Total Profit 432 Homework 4 438 Discussions 439 Oversampiing and Rare Event Detection 439 Discussion 446 Decision Matrix and the Profit Charts 446 Discussions 453 Micro-Target the Profitable Customers 453 Appendix 455 E. Credit Scoring Introduction: What Is Credit Scoring? 459 Credit Scoring: Business Objectives 460 Case Study: Consumer Credit Scoring 461 Description 461 Data Preparation 462 Feature Selection 462 STATISTICA Data Miner: Workhorses or Predictive Modeling 463 Overview: STATISTICA Data Miner Workspace 464 Analysis and Results 465 Decision Tree: CHAID 465 Classification Matrix: CHAID Model 467 TABLE OF CONTENTS Xl Comparative Assessment of the Models (Evaluation) 467 Classification Matrix: Boosting Trees with Deployment Model (Best Model) 469 Deploying the Model for Prediction 469 Conclusion 470 Objectives Steps 472 F. Churn Analysis 471 G. Text Mining: Automobile Brand Review Introduction 481 Text Mining 482 Input Documents 482 Selecting Input Documents 482 Stop Lists, Synonyms, and Phrases 482 Stemming and Support for Different Languages 483 Indexing of Input Documents: Scalability of STATISTICA Text Mining and Document Retrieval 483 Results, Summaries, and Transformations 483 Car Review Example 484 Saving Results into Input Spreadsheet 498 Interactive Trees (C&RT, CHAID) 503 Other Applications of Text Mining 512 Conclusion 512 HL Predictive Process Control: QC Data Mining Predictive Process Control Using ЅТАПЅПСА and STATISTICA Qc-miner 513 Case Study: Predictive Process Control 514 Understanding Manufacturing Processes 514 Data File: ProcessControl.sta 515 Variable Information 515 Problem Definition 515 Design Approaches 515 Data Analyses with STATISTICA 517 Split Input Data into the Training and Testing Sample 517 Stratified Random Sampling 517 Feature Selection and Root Cause Analyses 517 Different Models Used for Prediction 518 Compute Overlaid Lift Charts from All Models: Static Analyses 520 Classification Trees: CHAID 521 Compute Overlaid Lift/Gain Charts from All Models: Dynamic Analyses 523 Cross-Tabulation Matrix 524 Comparative Evaluation of Models: Dynamic Analyses 526 Gains Analyses by Deciles: Dynamic Analyses 526 Transformation of Change 527 Feature Selection and Root Cause Analyses 528 Interactive Trees: C&RT 528 Conclusion 529 I. Business Administration in a Medical Industry }. Clinical Psychology: Making Decisions about Best Therapy for a Client K. Education-Leadership Training for Business and Education L Dentistry: Facial Pain Study M. Profit Analysis of the German Credit Data Introduction 651 Modeling Strategy 653 SAS-EM 5.3 Interface 654 A Primer of SAS -ЕМ Predictive Modeling 654 Advanced Techniques of Predictive Modeling 669 MicrO Target the Profitable Customers 676 Appendix 678 N. Predicting Self-Reported Health Status Using Artificial Neural Networks Background 681 Data 682 Preprocessing and Filtering 683 Xli TABLE OF CONTENTS Part 1: Using a Wrapper Approach ín Weka to Determine the Most Appropriate Variables for Your Neural Network Model 684 Part 2: Taking the Results from, the Wrapper Approach in Weka into STATISTICA Data Miner to Do Neural Network Analyses 691 MEASURING TRUE COMPLEXITY, THE RIGHT MODEL FOR THE RIGHT USE, TOP MISTAKES, AND THE FUTURE OF ANALYTICS IS. Model Complexity (and How Ensembles Help) Preamble 707 Model Ensembles 708 Complexity 710 Generalized Degrees of Freedom 713 Examples: Decision Tree Surface with Noise 714 Summary and Discussion 719 Postscript 720 19. The Right Model for the Right Purpose: When Less Is Good Enough Preamble 723 More Is not Necessarily Better: Lessons from Nature and Engineering 724 Embrace Change Rather Than Flee from It 725 Decision Making Breeds True in the Business Organism 725 Muscles in the Business Organism 726 What Is a Complex System? 726 The 80:20 Rule in Action 728 Agile Modeling: An Example of How to Craft Sufficient Solutions 728 Postscript 730 20. Top 10 Data Mining Mistakes Preamble 733 Introduction 734 0. Lack Data 734 1. Focus on Training 735 2. Rely on One Technique 736 3. Ask the Wrong Question 738 4. Listen (Only) to the Data 739 5. Accept Leaks from the Future 742 6. Discount Pesky Cases 743 7. Extrapolate 744 8. Answer Every Inquiry 747 9. Sample Casually 750 10. Believe the Best Model 752 How Shall We Then Succeed? 753 Postscript 753 21. Prospects for the Future of Data Mining and Text Mining as Part of Our Everyday Lives Preamble 755 RFID 756 Social Networking and Data Mining 757 Example 1 758 Example 2 759 Example 3 760 Example 4 761 Image and Object Data Mining 761 Visual Data Preparation for Data Mining: Taking Photos, Moving Pictures, and Objects into Spreadsheets Representing the Photos, Moving Pictures, and Objects 765 Cloud Computing 769 What Can Science Learn from Googk? 772 The Next Generation of Data Mining 772 From the Desktop to the Clouds ... 778 Postscript 778 22. Summary: Our Design Preamble 781 Beware of Overtrained Models 782 A Diversity of Models and Techniques Is Best 783 The Process Is More Important Than the Tool 783 TABLE OF CONTENTS хш Text Mining of Unstructured Data Is Becoming Very Important 784 Practice Thinking About Your Organization as Organism Rather Than as Machine 784 Good Solutions Evolve Rather Than Just Appear After Initial Efforts 785 What You Don t Do Is Just as Important as What You Do 785 Very Intuitive Graphical Interfaces Are Replacing Procedural Programming 786 Data Mining Is No Longer a Boutique Operation; It Is Firmly Established in the Mainstream of Our Society 786 Smart Systems Are the Direction in Which Data Mining Technology Is Going 787 Postscript 787 Glossary 789 Index 801 DVD Install Instructions 823
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owner_facet	DE-945 DE-473 DE-BY-UBG DE-20 DE-91 DE-BY-TUM DE-634 DE-898 DE-BY-UBR DE-739 DE-703 DE-355 DE-BY-UBR DE-824 DE-384 DE-11 DE-858 DE-N32 DE-859 DE-N2
physical	XXXIV, 824 S. Ill., graph. Darst. DVD (12 cm)
publishDate	2009
publishDateSearch	2009
publishDateSort	2009
publisher	Elsevier AP
record_format	marc
spelling	Nisbet, Robert 1942- Verfasser (DE-588)140490930 aut Handbook of statistical analysis and data mining applications Robert Nisbet ; John Elder ; Gary Miner Amsterdam [u.a.] Elsevier AP 2009 XXXIV, 824 S. Ill., graph. Darst. DVD (12 cm) txt rdacontent n rdamedia nc rdacarrier Exploration de données (Informatique) - Méthodes statistiques Statistik (DE-588)4056995-0 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s Statistik (DE-588)4056995-0 s b DE-604 Elder, John F. 1961- Verfasser (DE-588)140491279 aut Miner, Gary 1942- Verfasser (DE-588)140491813 aut Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017539888&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Nisbet, Robert 1942- Elder, John F. 1961- Miner, Gary 1942- Handbook of statistical analysis and data mining applications Exploration de données (Informatique) - Méthodes statistiques Statistik (DE-588)4056995-0 gnd Data Mining (DE-588)4428654-5 gnd
subject_GND	(DE-588)4056995-0 (DE-588)4428654-5
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 Robert Nisbet ; John Elder ; Gary Miner
title_fullStr	Handbook of statistical analysis and data mining applications Robert Nisbet ; John Elder ; Gary Miner
title_full_unstemmed	Handbook of statistical analysis and data mining applications Robert Nisbet ; John Elder ; Gary Miner
title_short	Handbook of statistical analysis and data mining applications
title_sort	handbook of statistical analysis and data mining applications
topic	Exploration de données (Informatique) - Méthodes statistiques Statistik (DE-588)4056995-0 gnd Data Mining (DE-588)4428654-5 gnd
topic_facet	Exploration de données (Informatique) - Méthodes statistiques Statistik Data Mining
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017539888&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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