Visual and Spatial Analysis: Advances in Data Mining, Reasoning, and Problem Solving

581 Pages·2004·13.631 MB·English

by  Boris Kovalerchuk (auth.), Boris Kovalerchuk, James Schwing (eds.)

VISUAL AND SPATIAL ANALYSIS
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
James Schwing
Central Washington University,
Ellensburg, WA,
U.S.A.
A C.I.P. Catalogue record for this book is available from the Library of Congress.
ISBN 1-4020-2939-X (HB)
ISBN 1-4020-2958-6 (e-Book)
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TABLE OF CONTENTS

Preface xiii

PART 1. VISUAL PROBLEM SOLVING AND
DECISION MAKING

1. Decision process and its visual aspects
Boris Kovalerchuk

1. Current trends.....................................................................................3
2. Categories of visuals..........................................................................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 of visualization software........................................................43
6. Conclusion........................................................................................44
7. Acknowledgements..........................................................................44
8. Exercises and problems....................................................................44
9. References........................................................................................45
vi
PART 2. VISUAL AND HETEROGENEOUS
REASONING

3. Visual reasoning and representation
Boris Kovalerchuk

1. Visual vs. verbal reasoning...............................................................49
2. Iconic reasoning...............................................................................51
3. Diagrammatic reasoning...................................................................54
4. Heterogeneous reasoning..................................................................62
5. Geometric reasoning.........................................................................64
6. Explanatory vs. deductive reasoning................................................66
7. Application domains.........................................................................67
8. Human and model-based visual reasoning and representations.......70
9. Conclusion........................................................................................74
10. Exercises and problems....................................................................74
11. References........................................................................................75

4. Representing visual decision making: a computational
architecture for heterogeneous reasoning
Dave Barker-Plummer and John Etchemendy

1. Introduction......................................................................................79
2. Sentential natural deduction.............................................................81
3. Generalizing to heterogeneous deduction.........................................86
4. Generalizing to heterogeneous reasoning........................................95
5. Applications of the architecture......................................................105
6. Conclusions and further work........................................................106
7. Exercises and problems..................................................................107
8. References......................................................................................108

5. Algebraic visual symbolism for problem solving:
iconic equations from Diophantus to the present
Boris Kovalerchuk and James Schwing

1. Visual symbolism vs. text...............................................................111
2. Solving iconic equations and linear programming tasks................120
3. Conclusion......................................................................................127
4. Exercises and problems..................................................................127
5. References......................................................................................128
vii

6. Iconic reasoning architecture for analysis and decision
making
Boris Kovalerchuk

1. Introduction....................................................................................129
2. Storytelling iconic reasoning architecture......................................131
3. Hierarchical iconic reasoning.........................................................137
4. Consistent combined iconic reasoning...........................................139
5. Related work...................................................................................145
6. Conclusion......................................................................................149
7. Exercises and problems..................................................................150
8. References......................................................................................151

7. Toward visual reasoning and discovery: lessons from
the early history of mathematics
Boris Kovalerchuk

1. Introduction....................................................................................153
2. Visualization as illustration: lessons from hieroglyphic numerals.155
3. Visual reasoning: lessons from hieroglyphic arithmetic................162
4. Visual discovery: lessons from the discovery of π.........................164
5. Conclusion......................................................................................167
6. Exercises and problems..................................................................169
7. References......................................................................................170

PART 3. VISUAL CORRELATION

8. Visual correlation methods and models
Boris Kovalerchuk

1. Introduction....................................................................................175
2. Examples of numeric visual correlations........................................181
3. Classification of visual correlation methods..................................189
4. Visual correlation efficiency..........................................................191
5. Visual correlation: formal definitions, analysis, and theory..........193
6. Conclusion......................................................................................202
7. Acknowledgements........................................................................203
8. Exercises and problems..................................................................203
9. References......................................................................................203
viii

9. Iconic approach for data annotating, searching and
correlating
Boris Kovalerchuk

1. Introduction....................................................................................207
2. Iconic queries.................................................................................210
3. Composite icons.............................................................................213
4. Military iconic language.................................................................215
5. Iconic representations as translation invariants..............................219
6. Graphical coding principles............................................................220
7. Perception and optimal number of graphical elements..................224
8. Conclusion......................................................................................227
9. Acknowledgments..........................................................................228
10. Exercises and problems..................................................................228
11. References......................................................................................228

10. Bruegel iconic correlation system
Boris Kovalerchuk, Jon Brown, and Michael Kovalerchuk

1. Introduction....................................................................................231
2. The main concepts of the Bruegel iconic system...........................232
3. Dynamic icon generation for visual correlation.............................237
4. The Bruegel iconic language for automatic icon generation..........243
5. Case studies: correlating terrorism events......................................247
6. Case studies: correlating files and criminal events........................254
7. Case studies: market and health care.............................................256
8. Conclusions....................................................................................259
9. Acknowledgments..........................................................................260
10. Exercises and problems..................................................................260
11. References......................................................................................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. Introduction....................................................................................265
2. Related work...................................................................................267
3. Demonstration dataset and preprocessing......................................268
4. Multidimensional scaling...............................................................270
ix

5. Adaptive visualization using stratification.....................................271
6. Data stratification options and results............................................274
7. Scatterplot similarity matching.......................................................278
8. Incremental visualization using fusion...........................................280
9. Combined visualization technique.................................................286
10. Discussion and future work............................................................287
11. Conclusions....................................................................................298
12. Acknowledgments..........................................................................289
13. Exercises and problems..................................................................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
6. Acknowledgements........................................................................315
7. Exercises and problems..................................................................315
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
6. Acknowledgements........................................................................331
7. Exercises and problems..................................................................332
8. References......................................................................................332
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. GMDH-type neural networks.........................................................348
5. Neural-network decision trees........................................................355
6. A rule extraction technique............................................................366
7. Conclusion......................................................................................368
8. Acknowledgments..........................................................................368
9. Exercises and problems..................................................................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
9. Exercises and problems..................................................................385
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
8. Exercises and problems..................................................................405
9. References......................................................................................406
xi

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
7. Acknowledgements........................................................................432
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

1. 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-level conflation based on mutual information...459
8. Conclusion......................................................................................470
9. Acknowledgements........................................................................471
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 algorithms........................................................491
4. Conflation measures.......................................................................500
5. Generalization: image structural similarity....................................507