Visual and Spatial Analysis

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Visual and Spatial Analysis

Advances in Data Mining,

Reasoning, and Problem Solving

Edited by

Boris Kovalerchuk

Central Washington University, Ellensburg, WA,

U.S.A.

and

J

ames Schwing

Central Washington University, Ellensburg, WA,

U.S.A.

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Preface

PART

1.

VISUAL PROBLEM SOLVING AND

DECISION MAKING

1. Decision process and its visual aspects

Boris Kovalerchuk

xiii

1. Current trends 3

2. Categories ofvisuals 9

3. A modeling approach 13

4. DMM model and discovery of relations 16

5. Conceptual definitions 20

6. Visualization for browsing, searching, and decision making 24

7. Task-driven approach to visualization 25

8. Conclusion 27

9. Acknowledgements 27

10. Exercises and problems 27

11. References 28

2. Information visualization value stack model

Stephen G. Eick

1. The information visualization value stack problem 31 2. Where does information create high value? 32 3. Information visualization sweet spot model.. .40 4. Successful deployment models for information visualizations .42

5. Users ofvisualization software 43

6. Conclusion 44

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VI

PART 2. VISUAL AND HETEROGENEOUS

REASONING

3. Visual reasoning and representation

Boris Kovalerchuk

1. Visual vs. verbal reasoning .49

2. Ieonie reasoning 51

3. Diagrammatic reasoning 54

4. Heterogeneous reasoning 62

5. Geometrie reasoning 64

6. Explanatory vs. deduetive reasoning 66

7. Application domains 67

8. Human and model-based visual reasoning and representations 70

9. Conclusion 74

10. Exercises and prob lems 74

11. References 75

4. Representing visual decision making: a computational

architecture for heterogeneous reasoning

Dave Barker-Plummer and lohn Etehemendy

1. Introduetion 79

2. Sentential natural deduction 81

3. Generalizing to heterogeneous deduction 86 4. Generalizing to heterogeneous reasoning 95

5. Applications ofthe architecture 105

6. Conclusions and further work 106

8. References 108

5. Algebraic visual symbolism for problem solving:

iconic equations from Diophantus to

the present

Boris Kovalerchuk and James Sehwing

1. Visual symbolism vs. text... 111

2. Solving iconic equations and linear programming tasks 120

3. Conclusion 127

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6. Iconic reasoning architecture for analysis and decision

making

Boris Kova1erchuk

1. Introduction 129

2. Storytelling iconic reasoning architecture 131

3. Hierarchica1 iconic reasoning 137

4. Consistent combined iconic reasoning 139

5. Re1ated work 145

6. Conclusion 149

8. References 151

7. Toward visual reasoning and discovery: lessons from

the early history of mathematics

Boris Kova1erchuk

1. Introduction 153

2. Visua1ization as illustration: 1essons from hierog1yphic numera1s.155 3. Visua1reasoning: 1essons from hierog1yphic arithmetic 162 4. Visua1 discovery: 1essons from the discovery of1t 164

5. Conc1usion 167

7. References 170

PART 3. VISUAL CORRELATION

8. Visual correlation methods and models

Boris Kova1erchuk

1. Introduction 175

2. Examp1es of numeric visua1 corre1ations 181 3. C1assification ofvisua1 corre1ation methods 189

4. Visua1 corre1ation efficiency 191

5. Visua1 correlation: formal definitions, analysis, and theory 193

6. Conc1usion 202

7. Acknow1edgements 203

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viii

9. Iconic approach for data annotating, searching and

correlating

Boris Kovalerchuk

I. Introduetion 207

2. Ieonie queries 210

3. Composite ieons 213

4. Military ieonie language 215

5. Iconic representations as translation invariants 219

6. Graphical eoding principles 220

7. Pereeption and optimal number of graphical elements 224

8. Conclusion 227

9. Aeknowledgments 228

10. Exereises and problems 228

11. Referenees 228

10. Bruegel iconic correlation system

Boris Kovalerchuk, Ion Brown, and Michael Kovalerchuk

1. Introduetion 231

2. The main eoneepts ofthe Bruegel ieonie system 232 3. Dynamic ieon generation for visua1 correlation 237 4. The Bruegel ieonie language for automatie ieon generation 243 5. Case studies: correlating terrorism events 247 6. Case studies: correlating files and criminal events 254

7. Case studies: market and health eare 256

8. Conclusions 259

9. Acknowledgrnents 260

10. Exereises and problems 260

11. Referenees 261

PART 4. VISUAL AND SPATIAL DATA MINING

11. Visualizing data streams

Pak Chung Wong, Harlan Foote, Dan Adams, Wendy Cowley,

L. Ruby Leung, and Jim Thomas

1. Introduetion 265

2. Related work 267

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5. Adaptive visualization using stratification 271 6. Data stratification options and results 274

7. Scatterplot similarity matehing 278

8. Incremental visualization using fusion 280

9. Combined visualization technique 286

10. Discussion and future work 287

11. Conclusions 298

12. Acknowledgments 289

14. References 289

12. SPIN! -

an enterprise architecture for data mining

and visual analysis of spatial data

Michael May and Alexandr Savinov

1. Introduction 293

2. The system overview 295

3. The system architecture 298

4. Analysis of spatial data 303

5. Conclusion 314

8. References 316

13. XML-based visualization and evaluation of data

mining results

Dietrich Wettschereck

1. Introduction 319

2. The Predictive model markup language 321

3. VizWiz: interactive visualization and evaluation 324

4. Related work 330

5. Discussion 331

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x

14. Neural-network techniques for visual mining clinical

electroencephalograms

Vitaly Schetinin, Joachim Schult, and Anatoly Brazhnikov

1. Introduction 335

2. Neural network based techniques 338

3. Evolving cascade neural networks 342

4. GMDR-type neural networks 348

5. Neural-network decision trees 355

6. A rule extraction technique 366

7. Conclusion 368

8. Acknowledgments 368

10. References 369

15. Visual data mining with simultaneous rescaling

Evgenii Vityaev and Boris Kovalerchuk

1. Introduction 371

2. Definitions 374

3. Theorem on simultaneous scaling 375

4. A test example 377

5. Discovering simultaneous scaling 378

6. Additive structures in decision making 380

7. Physical structures 382

8. Conclusion 384

10. References 385

16. Visual data mining using monotone Boolean functions

Boris Kovalerchuk and Florian Delizy

1. Introduction 387

2. A method for visualizing data 390

3. Methods for visual data comparison 392

4. A method for visualizing pattern borders 395

5. Experiment with a Boolean data set 398

6. Data structures and formal definitions 403

7. Conclusion 404

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PART 5. VISUAL AND SPATIAL PROBLEM SOLVING

IN GEOSPATIAL DOMAINS

17. Imagery integration as conflict resolution decision

process: methods and approaches

Boris Kovalerchuk, James Schwing, and William Sumner

1. Introduction 409

2. Combining and resolving conflicts with geospatial datasets .411

3. Measures of decision correctness 422

4. Visualization 426

5. Conflict resolution by analytical and visual conflation agents 428

6. Conclusion 431

8. Exercises and problems .432

9. References 432

18. Multilevel analytical and visual decision framework

for imagery conflation and registration

George G. He, Boris Kovalerchuk, and Thomas Mroz

I. Introduction 435

2. Image inconsistencies .438

3. AVDM framework and complexities space .444

4. Conflation levels 446

5. Scenario of conflation .449

6. Rules for virtual imagery expert .454

7. Case study: pixel-Ievel conflation based on mutual information ...459

8. Conclusion 470

10. Exercises and problems .471

11. References 471

19. Conflation of images with algebraic structures

Boris Kovalerchuk, James Schwing, William Sumner, and Richard Chase

1. Introduction 473

2. Algebraic invariants .475

3. Feature correlating algorithrns 491

4. Conflation measures 500

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xii

6. Conclusion 507

8. Exercises 507

9. References 508

20. Algorithm development technology for conflation and

area-based conflation algorithm

Michael Kovalerchuk and Boris Kovalerchuk

I. Introduction 509

2. Steps of the algorithm development technology 512

3. Parameter identification steps 5l4

4. Attempt to formalize parameters 518

5. Analyze parameter invariance 520

6. Conflation algorithrn development 522

7. Determine conflatable images and algorithm limitations 528

8. Software and computational experiment 529

9. Conclusion 534

12. References 535

21. Virtual experts for imagery registration and conflation

Boris Kovalerchuk, Artemus Harper, Michael Kovalerchuk, and Jon Brown

1. Introduction 537

2. Shortcornings ofprevious attempts to deal with the subject 539

3. Goals and IVES System Architecture 542

4. Interactive on-the-fly analysis and recording 544

5. Multi-image knowledge extractor 546

6. Iconic Markup in IVES 551

7. Iconic ontological conflation 552

8. Conclusion 559

11. References 560

Visual and Spatial Analysis