Business modeling and data mining:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam [u.a.]
Morgan Kaufmann
2003
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXVI, 693 S. Ill., graph. Darst. |
| ISBN: | 155860653X |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1804130062399700992 |
|---|---|
| adam_text | Contents
Preface xvii
Part I A Map of the Territory
Chapter 1
The World, Knowledge, and Models 3
1.1 The Nature of the World 4
1.1.1 Events 6
1.1.2 Objects 7
1.1.3 Perception 8
1.1.4 Data 9
1.1.5 Structures 10
1.2 Systems 11
1.3 The Structure of Knowledge 13
1.3.1 The Problem of Knowing 14
1.3.2 Paradigms, Archetypes, Patterns, and Knowing 17
1.3.3 Frameworks for Representing Knowledge 21
1.3.4 Personal Knowledge 21
1.3.5 Social Knowledge 22
1.3.6 Other Types of Knowledge 22
1.4 Changing Knowledge Structures 24
1.4.1 Symbols and Symbolic Knowledge 25
1.4.2 Knowledge as a Network 26
1.4.3 Changing Evidence, Changing Conclusions 27
1.4.4 Aggregation and Catastrophe in Knowledge Structures 29
1.5 Summary 29
Supplemental Material 31
v
vi Contents
Chapter 2
Translating Experience 33
2.1 Mining and Ideas 34
2.1.1 Taking Data Apart 35
2.1.2 Data and Abstraction 37
2.1.3 Recognizing Patterns 39
2.1.4 Static Patterns 40
2.1.5 Dynamic Patterns 43
2.1.6 Novelty, Utility, Insight, and Interest 44
2.1.7 Mining and Pattern Seeking 45
2.2 Systems of the Worid 46
2.2.1 Open and Closed Form Systems and Solutions 47
2.2.2 The Nature of Systems 49
2.2.3 Coupling and Feedback 49
2.2.4 Systems Thinking 51
2.3 Strategies and ladies 53
2.3.1 Strategic versus Tactical Decisions and Actions 54
2.3.2 Dealing with Problems 55
2.3 3 Types of Uncertainty 56
2.3 4 Costs of Reducing Uncertainty 57
2.35 Deciding with Constrained Options 57
2.4 Summary 58
Chapter 3
Modeling and Mining: Putting It Together 61
3.1 Problems 62
3 11 Recognizing Problems 63
3 1.2 Describing Problems 64
3 1 3 Structuring Problems 65
3.1.4 Hidden Assumptions 65
3.2 Data about the World 66
3 2.1 The Nature of Data 67
3 2.2 Measurement and Description 67
3.2.3 Error and Confidence 68
3.3 Hypotheses: Explaining Data 70
3 3.1 Data Structures 70
3 3 2 Interaction and Relationship 71
333 Hypothesis and Explanation 72
Contents vii
3.4 Making Decisions 73
3.4.1 Frameworks for Decision: Representing Choices 74
3.4.2 Playing Games 76
3.4.3 Linear Programming 76
3.5 Deciding 77
3.5.1 Normative Decisions: What Should We Do? 78
3.5.2 Finding Possibilities: What Could We Do? 81
3.5.3 A Sketch Theory of Persistence and Change 82
3.6 Summary 87
Part II Business Modeling
Chapter 4
What Is a Model? 91
4.1 Introduction to Data, Information, and Knowledge 92
4.1.1 Data 92
4.1.2 Information 93
4.1.3 Knowledge 96
4.2 An Observer s Guide to Models 98
4.2.1 Inferential Models 98
4.2.2 Predictive Models 100
4.2.3 Associative Models 101
4.2.4 Systems Models 103
4.2.5 Static Models 105
4.2.6 Dynamic Models 106
4.2.7 Qualitative Models 108
4.2.8 Quantitative Models 109
4.2.9 Comparative Models 110
4.2.10 Interactive Models 112
4.2.11 Summarizing Model Types 113
4.3 Modeling as an Activity 114
4.3.1 Objectives 114
4.3.2 Empirical Modeling 115
4.3.3 Explaining Data 117
4.3.4 Modeling Assumptions 118
4.4 Summary 119
viii Contents
Chapter 5
Framing Business Models 121
5.1 Setting a Frame 124
5.1.1 Framing the Decision Process 125
5.2 Objectives: Getting your Bearings 127
5.3 Problems and Decisions 128
5.3.1 Decision Symbols 129
5.3 2 Decision Maps 132
5.3 3 Setting the Decision Frame 135
5.4 Modeling Situations: Connecting the Decision to a Worldview 136
5.5 Options: Assessing the Possible 137
5.5.1 Strategies 138
5.5.2 Tactics 139
5.5.3 Linking Strategy Payoffs 140
5.5.4 Threading Strategies Together 141
5 5 5 Mapping Options as Strategies 143
5.6 Expectations: Assessing the Future 145
5.6.1 Probably a Risky Business 145
5.6.2 Risk Selection 147
5.6.3 Satisfying Gains, Regrettable Losses 148
5.6.4 Benchmarks 149
5.6.5 Strategic Risk 150
5.7 Final Alignment 151
5.8 Mapping the Problem Frame 153
5.8.1 Wobbly Widgets 155
5.8.2 Mapping, Modeling, and Mining 156
5.9 Summary 157
5.10 Explanation of the Decision Map 157
5.11 Risk Calculations 159
511.1 Raw Risk 159
5.11.2 Biasing the Expectation: BRAVE 160
Chapter 6
Getting the Right Model 163
6.1 Interactive Exploration of the Territory 164
6.1.1 Stakeholders 165
6.1.2 Talking and Listening 169
Contents ix
6.2 Modeling the Business Situation Using Metaphors 174
6.2.1 The Systems Metaphor 176
6.2.2 Physical System Metaphors 187
6.3 Exploration Tools 195
6.3.1 Mind Maps 196
6.3.2 Cognitive Maps 197
6.3.3 Cognitive Models 199
6.4 The Business Case 204
6.4.1 What Is a Business Case? 205
6.4.2 Aligning the Business Case with Corporate Needs 207
6.4.3 Preparing the Business Case 209
6.4.4 Return on Investment (ROI) 211
6.4.5 Assembling and Presenting the Business Case 213
6.5 The Reality: What Can You Do with My Data? 215
6.5.1 Hunting for a Problem 216
6.5.2 Problem Opportunity: The Corporate Value Chain 216
6.5.3 Initial Project Size 219
6.6 Summary 220
Chapter 7
Getting the Model Right 221
7.1 Finding Data to Mine 221
7.1.1 External Data 222
7.1.2 Existing Data 223
7.1.3 Purpose Developed Data 224
7.2 Using Data 250
7.2.1 Types of Variables 251
7.2.2 Fusing Data Sets 252
7.3 Summary 258
Chapter 8
Deploying the Model 259
8.1 Modifying Business Processes 259
8.2 Motivation for Success 263
8.3 Impact of Model Types 264
8.3.1 Inferential Models: Delivering Explanations 264
8.3.2 Predictive Models 269
8.4 Summary 272
x Contents
Part III Data Mining
Chapter 9
Getting Started 275
9.1 Looking at Data 276
9.2 First Steps in Preparation: The Assay 278
9.2.1 Eyeballing Variables 279
9.2.2 Repairing Basic Problems with Variables 284
9.2.3 Basic Checks on the Data Set 288
9.3 Basic Feature Extraction 301
9.3 1 Representing Time, Distance, and Differential Relationships 302
9.3.2 Recoding 305
9.3.3 Representing Objects 307
9.4 Surveying Data 309
9.5 Summary 310
Chapter 10
What Mining Tools Do 313
10.1 Data Mining Algorithms 314
10.1.1 Variable Types and Their Impact on Algorithms 314
10.1.2 Characterizing Neighborhoods: Nearest Neighbors 316
10.1.3 Smooth Representations 331
10.1.4 Discontinuous and Non functional Representations 342
10.1.5 Algorithm Summary 346
10.2 Tools and Toolsets 347
10.2.1 Megaputer Intelligence (unvw.megaputer.com) 348
10.2.2 Angoss Knowledge Studio (unvw.angoss.com) 351
10.2.3 WizWhy (www.wizsoft.com) 352
10.2.4 Bayesware Discoverer (www.bayesware.com) 353
10.2.5 e (www.sdi inc.com) 355
10.2.6 Microsoft SQL Server 2000 (www.microsoft.com) 357
10.3 Summary 359
Contents xi
Chapter 11
Getting the Initial Model: Basic Practices of Data
Mining 361
11.1 Preparing to Stay Honest 362
11.2 Addressing the Data 365
11.2.1 Input and Output Data Set Configurations 365
11.2.2 Missing Value Check Model 373
11.2.3 Applied Honesty: Using Training and Test Data Sets 378
11.3 Modeling to Understand 380
11.3 1 Modeling for Understanding Using Decision Trees 382
11.3.2 Modeling for Understanding Using SOMs 386
11.3.3 Modeling for Understanding Using Linear Regression 390
11.3.4 Summary of Understanding Data Sets 394
11.4 Modeling to Classify 395
11.4.1 Balancing Data Sets 396
11.4.2 Building a Dichotomous Classification Model 398
11.4.3 Classification Errors 399
11.4.4 Classification by Score 402
11.4.5 Building a Continuous Classification Model 402
11.4.6 Building a Multiple Classification Model 405
11.4.7 Summary of Modeling to Classify 413
11.5 Modeling to Predict 414
11.5.1 Gathering Data for Prediction 4l6
11.5.2 Causality 420
11.5.3 Summary of Modeling to Predict 422
11.6 Summary 423
Supplemental Material 423
Chapter 12
Improving the Mined Model 427
12.1 Learning from Errors 429
12.1.1 Looking at Errors 430
. 12.1.2 Predicting Errors 433
xii Contents
12.1.3 Continuous Classifier Residuals 436
12.1.4 Continuous Classifier Residuals versus Actual Values Plot 440
12.1.5 Continuous Classifier Actual versus Predicted Values Plot 442
12.1.6 Continuous Classifier Variance Plot 444
12.1.7 Perfect Models 446
12.1.8 Summary of Classification Model Residual Checking 447
12.1.9 Improving Explanatory Models 449
12.2 Improving Model Quality, Solving Problems 450
12.2.1 Problem: The Data Doesn t Support the Model 452
12.2.2 Problem: The Data Partially Doesn t Support the Model 453
12.2.3 Problem: Reformatting Data 454
12.2.4 Problem: Respecifying the Algorithm 469
12.2.5 Problem: Insufficient Data 482
12.2.6 Problem: Uneven Data 484
12.2.7 Problem: Estimation Bias When Mining a Model 486
12.2.8 Problem: Noise Reduction 487
12.2.9 Problem: Categorical Correlation 490
12.2.10 Problem: Partial Colinearities 491
12.2.11 Problem: Data Not Representative of the Business Problem 492
12.2.12 Problem: Output Limiting 493
12.2.13 Problem: Variance Bias 493
12.2.14 Problem: The Modeling Tool Is Broken 494
12.2.15 Problem: Anachronistic Variables 495
12.2.16 Problem: Noisy or Irrelevant Variables 495
12.2.17 Problem: Interaction Effects 497
12.2.18 Problem: Insufficient Data 506
12.3 Summary 508
Chapter 13
Deploying the Mined Model 509
13.1 Deploying Explanatory Models 509
13.2 Novelty, and Keeping the Model Working 510
13.2.1 Regression to the Mean 510
13.2.2 Distributions 517
13.2.3 Distributions That Aren t 519
13.2.4 Detecting Novelty 521
13.2.5 Using the Novelty Detector 527
13.3 Deployed Model Form 528
13.4 Summary 528
Contents xiii
Part IV Methodology
Chapter 14
Methodology 533
14.1 Structure of the Methodologies 536
14.1.1 Action Boxes 536
14.1.2 Discovery Boxes 538
14.1.3 Technique Boxes 539
14.1.4 Example Boxes 540
14.1.5 Differences between the Printed and Downloadable Versions 540
14.2 Using the Methodology 540
14.2.1 Using Mil: The Modeling Methodology 541
14.2.2 Using Mill: The Mining Methodology 542
14.3 Caveats! 542
Mil
Modeling Methodology 545
Mill
Mining Methodology 571
Resources 665
Index 673
|
| any_adam_object | 1 |
| author | Pyle, Dorian |
| author_facet | Pyle, Dorian |
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| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV017211706 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T19:15:03Z |
| institution | BVB |
| isbn | 155860653X |
| language | English |
| lccn | 2002114338 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-010372349 |
| oclc_num | 52349252 |
| open_access_boolean | |
| owner | DE-634 DE-11 |
| owner_facet | DE-634 DE-11 |
| physical | XXVI, 693 S. Ill., graph. Darst. |
| publishDate | 2003 |
| publishDateSearch | 2003 |
| publishDateSort | 2003 |
| publisher | Morgan Kaufmann |
| record_format | marc |
| spelling | Pyle, Dorian Verfasser aut Business modeling and data mining Dorian Pyle Amsterdam [u.a.] Morgan Kaufmann 2003 XXVI, 693 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Datenverarbeitung Wirtschaft Business Data processing Management Data mining Database management Information resources management Management information systems Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Kundengruppenmanagement (DE-588)4197319-7 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Informationsmanagement (DE-588)4114012-6 gnd rswk-swf Managementinformationssystem (DE-588)4074518-1 gnd rswk-swf Betriebliches Informationssystem (DE-588)4069386-7 gnd rswk-swf Datenmanagement (DE-588)4213132-7 gnd rswk-swf Marketing (DE-588)4037589-4 gnd rswk-swf Kundengruppenmanagement (DE-588)4197319-7 s Marketing (DE-588)4037589-4 s DE-604 Betriebliches Informationssystem (DE-588)4069386-7 s Datenverarbeitung (DE-588)4011152-0 s Data Mining (DE-588)4428654-5 s Managementinformationssystem (DE-588)4074518-1 s Datenmanagement (DE-588)4213132-7 s Informationsmanagement (DE-588)4114012-6 s 1\p DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010372349&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Pyle, Dorian Business modeling and data mining Datenverarbeitung Wirtschaft Business Data processing Management Data mining Database management Information resources management Management information systems Datenverarbeitung (DE-588)4011152-0 gnd Kundengruppenmanagement (DE-588)4197319-7 gnd Data Mining (DE-588)4428654-5 gnd Informationsmanagement (DE-588)4114012-6 gnd Managementinformationssystem (DE-588)4074518-1 gnd Betriebliches Informationssystem (DE-588)4069386-7 gnd Datenmanagement (DE-588)4213132-7 gnd Marketing (DE-588)4037589-4 gnd |
| subject_GND | (DE-588)4011152-0 (DE-588)4197319-7 (DE-588)4428654-5 (DE-588)4114012-6 (DE-588)4074518-1 (DE-588)4069386-7 (DE-588)4213132-7 (DE-588)4037589-4 |
| title | Business modeling and data mining |
| title_auth | Business modeling and data mining |
| title_exact_search | Business modeling and data mining |
| title_full | Business modeling and data mining Dorian Pyle |
| title_fullStr | Business modeling and data mining Dorian Pyle |
| title_full_unstemmed | Business modeling and data mining Dorian Pyle |
| title_short | Business modeling and data mining |
| title_sort | business modeling and data mining |
| topic | Datenverarbeitung Wirtschaft Business Data processing Management Data mining Database management Information resources management Management information systems Datenverarbeitung (DE-588)4011152-0 gnd Kundengruppenmanagement (DE-588)4197319-7 gnd Data Mining (DE-588)4428654-5 gnd Informationsmanagement (DE-588)4114012-6 gnd Managementinformationssystem (DE-588)4074518-1 gnd Betriebliches Informationssystem (DE-588)4069386-7 gnd Datenmanagement (DE-588)4213132-7 gnd Marketing (DE-588)4037589-4 gnd |
| topic_facet | Datenverarbeitung Wirtschaft Business Data processing Management Data mining Database management Information resources management Management information systems Kundengruppenmanagement Data Mining Informationsmanagement Managementinformationssystem Betriebliches Informationssystem Datenmanagement Marketing |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010372349&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT pyledorian businessmodelinganddatamining |