Handbook of Statistical Analysis and Data Mining Applications (eBook)
864 Seiten
Elsevier Science (Verlag)
978-0-08-091203-5 (ISBN)
The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions.
- Written 'By Practitioners for Practitioners'
- Non-technical explanations build understanding without jargon and equations
- Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models
- Practical advice from successful real-world implementations
- Includes extensive case studies, examples, MS PowerPoint slides and datasets
- CD-DVD with valuable fully-working 90-day software included: 'Complete Data Miner - QC-Miner - Text Miner' bound with book
Dr. Robert Nisbet was trained initially in Ecology and Ecosystems Analysis. He has over 30 years of experience in complex systems analysis and modeling, most recently as a Researcher (University of California, Santa Barbara). In business, he pioneered the design and development of configurable data mining applications for retail sales forecasting, and Churn, Propensity-to-buy, and Customer Acquisition in Telecommunications, Insurance, Banking, and Credit industries. In addition to data mining, he has expertise in data warehousing technology for Extract, Transform, and Load (ETL) operations, Business Intelligence reporting, and data quality analyses. He is lead author of the 'Handbook of Statistical Analysis & Data Mining Applications” (Academic Press, 2009), and a co-author of 'Practical Text Mining' (Academic Press, 2012), and co-author of 'Practical Predictive Analytics and Decisioning Systems for Medicine (Academic Press, 2015). Currently, he serves as an Instructor in the University of California, Irvine Predictive Analytics Certificate Program, teaching online and on-campus courses in Effective Data preparation, and Applications of Predictive Analytics. Additionally Bob is in the last stages of writing another book on 'Data Preparation for Predictive Analytic Modeling.
The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions. - Written "e;By Practitioners for Practitioners"e;- Non-technical explanations build understanding without jargon and equations- Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models- Practical advice from successful real-world implementations- Includes extensive case studies, examples, MS PowerPoint slides and datasets- CD-DVD with valuable fully-working 90-day software included: "e;Complete Data Miner - QC-Miner - Text Miner"e; bound with book
Front Cover 1
Handbook of Statistical Analysis and Data Mining Applications 4
Copyright Page 5
Table of Contents 6
Foreword 1 16
Foreword 2 18
Preface 20
Introduction 24
List of Tutorials by Guest Authors 30
Part 1: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process 36
Chapter 1: The Background for Data Mining Practice 38
Preamble 38
A Short History of Statistics and Data Mining 39
Modern Statistics: A Duality? 40
Assumptions of the Parametric Model 41
Two Views of Reality 43
Aristotle 43
Plato 44
The Rise of Modern Statistical Analysis: The Second Generation 45
Data, Data Everywhere 46
Machine Learning Methods: The Third Generation 46
Statistical Learning Theory: The Fourth Generation 47
Postscript 48
References 49
Chapter 2: Theoretical Considerations for Data Mining 50
Preamble 50
The Scientific Method 51
What Is Data Mining? 52
A Theoretical Framework for the Data Mining Process 53
Microeconomic Approach 54
Inductive Database Approach 54
Strengths of the Data Mining Process 54
Customer-Centric Versus Account-Centric: A New Way to Look at Your Data 55
The Physical Data Mart 55
The Virtual Data Mart 56
Householded Databases 56
The Data Paradigm Shift 57
Creation of the CAR 57
Major Activities of Data Mining 58
Major Challenges of Data Mining 60
Examples of Data Mining Applications 61
Major Issues in Data Mining 61
General Requirements for Success in a Data Mining Project 63
Example of a Data Mining Project: Classify a Bat's Species by Its Sound 63
The Importance of Domain Knowledge 65
Postscript 65
Why Did Data Mining Arise? 65
Some Caveats with Data Mining Solutions 66
References 67
Chapter 3: The Data Mining Process 68
Preamble 68
The Science of Data Mining 68
The Approach to Understanding and Problem Solving 69
CRISP-DM 70
Business Understanding (Mostly Art) 71
Define the Business Objectives of the Data Mining Model 71
Assess the Business Environment for Data Mining 72
Formulate the Data Mining Goals and Objectives 72
Data Understanding (Mostly Science) 74
Data Acquisition 74
Data Integration 74
Data Description 75
Data Quality Assessment 75
Data Preparation (A Mixture of Art and Science) 75
Modeling (A Mixture of Art and Science) 76
Steps in the Modeling Phase of CRISP-DM 76
Deployment (Mostly Art) 80
Closing the Information Loop (Art) 81
The Art of Data Mining 81
Artistic Steps in Data Mining 82
Postscript 82
References 83
Chapter 4: Data Understanding and Preparation 84
Preamble 84
Activities of Data Understanding and Preparation 85
Definitions 85
Issues That Should be Resolved 86
Basic Issues That Must Be Resolved in Data Understanding 86
Basic Issues That Must Be Resolved in Data Preparation 86
Data Understanding 86
Data Acquisition 86
Data Extraction 88
Data Description 89
Data Assessment 91
Data Profiling 91
Data Cleansing 91
Data Transformation 92
Data Imputation 94
Data Weighting and Balancing 97
Data Filtering and Smoothing 99
Data Abstraction 101
Data Reduction 104
Data Sampling 104
Data Discretization 108
Data Derivation 108
Postscript 110
References 110
Chapter 5: Feature Selection 112
Preamble 112
Variables as Features 113
Types of Feature Selections 113
Feature Ranking Methods 113
Gini Index 113
Bi-variate Methods 115
Multivariate Methods 115
Complex Methods 117
Subset Selection Methods 117
The Other Two Ways of Using Feature Selection in STATISTICA: Interactive Workspace 128
STATISTICA DMRecipe Method 128
Postscript 131
References 132
Chapter 6: Accessory Tools for Doing Data Mining 134
Preamble 134
Data Access Tools 135
Structured Query Language (SQL) Tools 135
Extract, Transform, and Load (ETL) Capabilities 135
Data Exploration Tools 136
Basic Descriptive Statistics 136
Combining Groups (Classes) for Predictive Data Mining 140
Slicing/Dicing and Drilling Down into Data Sets/Results Spreadsheets 141
Modeling Management Tools 142
Data Miner Workspace Templates 142
Modeling Analysis Tools 142
Feature Selection 142
Importance Plots of Variables 143
In-Place Data Processing (IDP) 148
Example: The IDP Facility of STATISTICA Data Miner 149
How to Use the SQL 149
Rapid Deployment of Predictive Models 149
Model Monitors 151
Postscript 152
Bibliography 152
Part 2: The Algorithms in Data Mining and Text Mining, the Organization of the Three most common Data Mining Tools, and Selected Speci... 154
Chapter 7: Basic Algorithms for Data Mining: A Brief Overview 156
Preamble 156
STATISTICA Data Miner Recipe (DMRecipe) 158
KXEN 159
Basic Data Mining Algorithms 161
Association Rules 161
Neural Networks 163
Radial Basis Function (RBF) Networks 171
Automated Neural Nets 173
Generalized Additive Models (GAMs) 173
Outputs of GAMs 174
Interpreting Results of GAMs 174
Classification and Regression Trees (CART) 174
Recursive Partitioning 179
Pruning Trees 179
General Comments about CART for Statisticians 179
Advantages of CART over Other Decision Trees 180
Uses of CART 181
General Chaid Models 181
Advantages of CHAID 182
Disadvantages of CHAID 182
Generalized EM and k-Means Cluster Analysis-An Overview 182
k-Means Clustering 182
EM Cluster Analysis 183
Processing Steps of the EM Algorithm 184
V-fold Cross-Validation as Applied to Clustering 184
Postscript 185
References 185
Bibliography 185
Chapter 8: Advanced Algorithms for Data Mining 186
Preamble 186
Advanced Data Mining Algorithms 189
Interactive Trees 189
Multivariate Adaptive Regression Splines (MARSplines) 193
Statistical Learning Theory: Support Vector Machines 197
Sequence, Association, and Link Analyses 199
Independent Components Analysis (ICA) 203
Kohonen Networks 204
Characteristics of a Kohonen Network 204
Quality Control Data Mining and Root Cause Analysis 204
Image and Object Data Mining: Visualization and 3D-Medical and Other Scanning Imaging 205
Postscript 206
References 206
Chapter 9: Text Mining and Natural Language Processing 208
Preamble 208
The Development of Text Mining 209
A Practical Example: NTSB 210
Goals of Text Mining of NTSB Accident Reports 219
Drilling into Words of Interest 223
Means with Error Plots 224
Feature Selection Tool 225
A Conclusion: Losing Control of the Aircraft in Bad Weather Is Often Fatal 226
Summary 229
Text Mining Concepts Used in Conducting Text Mining Studies 229
Postscript 229
References 230
Chapter 10: The Three Most Common Data Mining Software Tools 232
Preamble 232
SPSS Clementine Overview 232
Overall Organization of Clementine Components 233
Organization of the Clementine Interface 234
Clementine Interface Overview 234
Setting the Default Directory 236
SuperNodes 236
Execution of Streams 237
SAS-Enterprise Miner (SAS-EM) Overview 238
Overall Organization of SAS-EM Version 5.3 Components 238
Layout of the SAS-Enterprise Miner Window 239
Various SAS-EM Menus, Dialogs, and Windows Useful During the Data Mining Process 240
Software Requirements to Run SAS-EM 5.3 Software 241
STATISTICA Data Miner, QC-Miner, and Text Miner Overview 249
Overall Organization and Use of STATISTICA Data Miner 249
Three Formats for Doing Data Mining in STATISTICA 265
Postscript 269
References 269
Chapter 11: Classification 270
Preamble 270
What Is Classification? 270
Initial Operations in Classification 271
Major Issues with Classification 271
What Is the Nature of the Data Set to Be Classified? 271
How Accurate Does the Classification Have to Be? 271
How Understandable Do the Classes Have to Be? 272
Assumptions of Classification Procedures 272
Numerical Variables Operate Best 272
No Missing Values 272
Variables Are Linear and Independent in Their Effects on the Target Variable 272
Methods for Classification 273
Nearest-Neighbor Classifiers 274
Analyzing Imbalanced Data Sets with Machine Learning Programs 275
CHAID 281
Random Forests and Boosted Trees 283
Logistic Regression 285
Neural Networks 286
Naive Bayesian Classifiers 288
What Is the Best Algorithm for Classification? 291
Postscript 292
References 293
Chapter 12: Numerical Prediction 294
Preamble 294
Linear Response Analysis and the Assumptions of the Parametric Model 295
Parametric Statistical Analysis 296
Assumptions of the Parametric Model 297
The Assumption of Independency 297
The Assumption of Normality 297
Normality and the Central Limit Theorem 298
The Assumption of Linearity 299
Linear Regression 299
Methods for Handling Variable Interactions in Linear Regression 300
Collinearity among Variables in a Linear Regression 300
The Concept of the Response Surface 301
Generalized Linear Models (GLMs) 305
Methods for Analyzing Nonlinear Relationships 306
Nonlinear Regression and Estimation 306
Logit and Probit Regression 307
Poisson Regression 307
Exponential Distributions 307
Piecewise Linear Regression 308
Data Mining and Machine Learning Algorithms Used in Numerical Prediction 309
Numerical Prediction with C& RT
Model Results Available in C& RT
Advantages of Classification and Regression Trees (C& RT) Methods
General Issues Related to C& RT
Application to Mixed Models 315
Neural Nets for Prediction 315
Manual or Automated Operation? 315
Structuring the Network for Manual Operation 315
Modern Neural Nets Are "Gray Boxes" 316
Example of Automated Neural Net Results 316
Support Vector Machines (SVMs) and Other Kernel Learning Algorithms 317
Postscript 319
References 319
Chapter 13: Model Evaluation and Enhancement 320
Preamble 320
Introduction 321
Model Evaluation 321
Splitting Data 322
Avoiding Overfit Through Complexity Regularization 323
Error Metric: Estimation 326
Error Metric: Classification 326
Error Metric: Ranking 328
Cross-Validation to Estimate Error Rate and Its Confidence 330
Bootstrap 331
Target Shuffling to Estimate Baseline Performance 332
Re-Cap of the Most Popular Algorithms 335
Linear Methods (Consensus Method, Stepwise Is Variable-Selecting) 335
Decision Trees (Consensus Method, Variable-Selecting) 335
Neural Networks (Consensus Method) 336
Nearest Neighbors (Contributory Method) 336
Clustering (Consensus or Contributory Method) 337
Enhancement Action Checklist 337
Ensembles of Models: The Single Greatest Enhancement Technique 339
Bagging 340
Boosting 340
Ensembles in General 341
How to Thrive as a Data Miner 342
Big Picture of the Project 342
Project Methodology and Deliverables 343
Professional Development 344
Three Goals 345
Postscript 346
References 346
Chapter 14: Medical Informatics 348
Preamble 348
What Is Medical Informatics? 348
How Data Mining and Text Mining Relate to Medical Informatics 349
XplorMed 351
ABView: HivResist 352
3D Medical Informatics 352
What Is 3D Informatics? 352
Future and Challenges of 3D Medical Informatics 353
Journals and Associations in the Field of Medical Informatics 353
Postscript 353
References 354
Bibliography 354
Chapter 15: Bioinformatics 356
Preamble 356
What Is Bioinformatics? 358
Data Analysis Methods in Bioinformatics 361
ClustalW2: Sequence Alignment 361
Searching Databases for RNA Molecules 362
Web Services in Bioinformatics 362
How Do We Apply Data Mining Methods to Bioinformatics? 364
Postscript 367
Tutorial Associated with This Chapter on Bioinformatics 367
Books, Associations, and Journals on Bioinformatics, and Other Resources, Including Online 367
References 368
Bibliography 369
Chapter 16: Customer Response Modeling 370
Preamble 370
Early CRM Issues in Business 371
Knowing How Customers Behaved Before They Acted 371
Transforming Corporations into Business Ecosystems: The Path to Customer Fulfillment 372
CRM in Business Ecosystems 373
Differences Between Static Measures and Evolutionary Measures 373
How Can Human Nature as Viewed Through Plato Help Us in Modeling Customer Response? 374
How Can We Reorganize Our Data to Reflect Motives and Attitudes? 374
What Is a Temporal Abstraction? 375
Conclusions 379
Postscript 380
References 380
Chapter 17: Fraud Detection 382
Preamble 382
Issues with Fraud Detection 383
Fraud Is Rare 383
Fraud Is Evolving 383
Large Data Sets Are Needed 383
The Fact of Fraud Is Not Always Known During Modeling 383
When the Fraud Happened Is Very Important to Its Detection 384
Fraud Is Very Complex 384
Fraud Detection May Require the Formulation of Rules Based on General Principles,"Red Flags," Alerts, and Profiles 384
Fraud Detection Requires Both Internal and External Business Data 384
Very Few Data Sets and Modeling Details Are Available 385
How Do You Detect Fraud? 385
Supervised Classification of Fraud 386
How Do You Model Fraud? 387
How Are Fraud Detection Systems Built? 388
Intrusion Detection Modeling 390
Comparison of Models with and Without Time-Based Features 390
Building Profiles 395
Deployment of Fraud Profiles 395
Postscript and Prolegomenon 396
References 396
Part 3: Tutorials-Step-by-Step Case Studies as a Starting Point to learn how to do Data Mining Analyses 398
Guest Authors of the Tutorials 400
Tutorial A: How to Use Data Miner Recipe STATISTICA Data Miner Only 402
What Is STATISTICA Data Miner Recipe (DMR)? 408
Core Analytic Ingredients 408
Tutorial B: Data Mining for Aviation Safety Using Data Mining Recipe
412
Airline Safety 413
SDR Database 414
Preparing the Data for Our Tutorial 417
Data Mining Approach 418
Data Mining Algorithm Error Rate 421
Conclusion 422
References 424
Tutorial C: Predicting Movie Box-Office Receipts Using SPSS Clementine Data
426
Introduction 426
Data and Variable Definitions 427
Getting to Know the Workspace of the Clementine Data Mining Toolkit 428
Results 431
Publishing and Reuse of Models and Other Outputs 439
References 450
Tutorial D: Detecting Unsatisfied Customers: A Case Study Using SAS Enterprise Miner
452
Introduction 453
The Data 453
The Objectives of the Study 453
SAS-EM 5.3 Interface 454
A Primer of SAS-EM Predictive Modeling 455
Homework 1 465
Discussions 466
Homework 2 466
Homework 3 466
Scoring Process and the Total Profit 467
Homework 4 473
Discussions 474
Oversampling and Rare Event Detection 474
Discussion 481
Decision Matrix and the Profit Charts 481
Discussions 488
Micro-Target the Profitable Customers 488
Appendix 490
Reference 493
Tutorial E: Credit Scoring Using STATISTICA Data
494
Introduction: What Is Credit Scoring? 494
Credit Scoring: Business Objectives 495
Case Study: Consumer Credit Scoring 496
Description 496
Data Preparation 497
Feature Selection 497
STATISTICA Data Miner: "Workhorses" or Predictive Modeling 498
Overview: STATISTICA Data Miner Workspace 499
Analysis and Results 500
Decision Tree: CHAID 500
Classification Matrix: CHAID Model 502
Comparative Assessment of the Models (Evaluation) 502
Classification Matrix: Boosting Trees with Deployment Model (Best Model) 504
Deploying the Model for Prediction 504
Conclusion 505
Tutorial F: Churn Analysis With SPSS-Clementine 506
Objectives 506
Steps 507
Tutorial G: Text Mining: Automobile Brand Review Using STATISTICA Data
516
Introduction 516
Text Mining 517
Input Documents 517
Selecting Input Documents 517
Stop Lists, Synonyms, and Phrases 517
Stemming and Support for Different Languages 518
Indexing of Input Documents: Scalability of STATISTICA Text Mining and Document Retrieval 518
Results, Summaries, and Transformations 518
Car Review Example 519
Saving Results into Input Spreadsheet 533
Interactive Trees (C& RT, CHAID)
Other Applications of Text Mining 547
Conclusion 547
Tutorial H: Predictive Process Control: QC-Data Mining Using STATISTICA Data Miner
548
Predictive Process Control Using STATISTICA and STATISTICA QC-Miner 548
Case Study: Predictive Process Control 549
Understanding Manufacturing Processes 549
Data File: ProcessControl.sta 550
Variable Information 550
Problem Definition 550
Design Approaches 550
Data Analyses with STATISTICA 552
Split Input Data into the Training and Testing Sample 552
Stratified Random Sampling 552
Feature Selection and Root Cause Analyses 552
Different Models Used for Prediction 553
Compute Overlaid Lift Charts from All Models: Static Analyses 555
Classification Trees: CHAID 556
Compute Overlaid Lift/Gain Charts from All Models: Dynamic Analyses 558
Cross-Tabulation Matrix 559
Comparative Evaluation of Models: Dynamic Analyses 561
Gains Analyses by Deciles: Dynamic Analyses 561
Transformation of Change 562
Feature Selection and Root Cause Analyses 563
Interactive Trees: C& RT
Conclusion 564
Tutorials I, J, AND K: Three Short Tutorials Showing the Use of Data Mining and Particularly C& RT to Predict and Display Possible Structural Relationships among Data
Tutorial I: Business Administration in a Medical Industry: Determining Possible
568
Tutorial J: Clinical Psychology: Making Decisions about Best Therapy for a Client: Using Data Mining to Explore the
602
Tutorial K: Education-Leadership Training for Business and Education Using C& RT to Predict and
622
References 656
Tutorial L: Dentistry: Facial Pain Study Based on 84 Predictor Variables
658
Tutorial M: Profit Analysis of the German Credit Data Using SAS-EM Version 5.3 686
Introduction 686
Modeling Strategy 688
SAS-EM 5.3 Interface 689
A Primer of SAS-EM Predictive Modeling 689
Discussions 704
Advanced Techniques of Predictive Modeling 704
Conclusion 711
Micro-Target the Profitable Customers 711
Appendix 713
References 715
Tutorial N: Predicting Self-Reported Health Status Using Artificial Neural Networks 716
Background 716
Data 717
Preprocessing and Filtering 718
Part 1: Using a Wrapper Approach in Weka to Determine the Most Appropriate Variables for Your Neural Network Model 719
Part 2: Taking the Results from the Wrapper Approach in Weka into STATISTICA Data Miner to do Neural Network Analyses 726
References 738
Part 4: Measuring true complexity, the "Right Model for the Right Use," Top Mistakes, and the Future of Analytics 740
Chapter 18: Model Complexity (and How Ensembles Help) 742
Preamble 742
Model Ensembles 743
Complexity 745
Generalized Degrees of Freedom 748
Examples: Decision Tree Surface with Noise 749
Summary and Discussion 754
Postscript 755
References 755
Chapter 19: The Right Model for the Right Purpose: When Less Is Good Enough 758
Preamble 758
More Is Not Necessarily Better: Lessons from Nature and Engineering 759
Embrace Change Rather Than Flee from It 760
Decision Making Breeds True in the Business Organism 760
Muscles in the Business Organism 761
What Is a Complex System? 761
The 80:20 Rule in Action 763
Agile Modeling: An Example of How to Craft Sufficient Solutions 763
Postscript 765
References 766
Chapter 20: Top 10 Data Mining Mistakes 768
Preamble 768
Introduction 769
0. Lack Data 769
1. Focus on Training 770
2. Rely on One Technique 771
3. Ask the Wrong Question 773
4. Listen (Only) to the Data 774
5. Accept Leaks from the Future 777
6. Discount Pesky Cases 778
7. Extrapolate 779
8. Answer Every Inquiry 782
9. Sample Casually 785
10. Believe the Best Model 787
How Shall We Then Succeed? 788
Postscript 788
References 788
Chapter 21: Prospects for the Future of Data Mining and Text Mining as Part of Our Everyday Lives 790
Preamble 790
RFID 791
Social Networking and Data Mining 792
Example 1 793
Example 2 794
Example 3 795
Example 4 796
Image and Object Data Mining 796
Visual Data Preparation for Data Mining: Taking Photos, Moving Pictures, and Objects into Spreadsheets Representing the Photos... 800
Cloud Computing 804
The Next Generation of Data Mining 807
From the Desktop to the Clouds 813
Postscript 813
References 813
Chapter 22: Summary: Our Design 816
Preamble 816
Beware of Overtrained Models 817
A Diversity of Models and Techniques Is Best 818
The Process Is More Important Than the Tool 818
Text Mining of Unstructured Data Is Becoming Very Important 819
Practice Thinking about Your Organization as Organism Rather Than as Machine 819
Good Solutions Evolve Rather Than Just Appear after Initial Efforts 820
What You Don't Do Is Just as Important as What You Do 820
Very Intuitive Graphical Interfaces Are Replacing Procedural Programming 821
Data Mining Is No Longer a Boutique Operation It Is Firmly Established in the Mainstream of Our Society
"Smart" Systems Are the Direction in which Data Mining Technology is Going 822
Postscript 822
References 823
Glossary 824
Index 836
DVD Install Instructions
858
Installing STATISTICA 858
| Erscheint lt. Verlag | 14.5.2009 |
|---|---|
| Sprache | englisch |
| Themenwelt | Geisteswissenschaften |
| Informatik ► Datenbanken ► Data Warehouse / Data Mining | |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| Sozialwissenschaften ► Pädagogik | |
| Technik | |
| ISBN-10 | 0-08-091203-6 / 0080912036 |
| ISBN-13 | 978-0-08-091203-5 / 9780080912035 |
| Haben Sie eine Frage zum Produkt? |
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