Daten, Visualisierung und Visual Analytics
Big Data ist ein Schlagwort unserer Zeit, denn es entstehen Daten fast bei jedem Prozess, ob in Wirtschaft, Wissenschaft, oder im sozialen Bereich.

In den letzten Jahren ist es immer einfacher geworden solche Daten persistent zu sammeln, aufgrund moderner Speichertechnologien. Deren Auswertung hat sich jedoch zunehmend als schwierig erwiesen.

Zum einen fehlt es oft an geeigneten Methoden, zum anderen hat sich gezeigt, das Daten auch innerhalb ihrer Eigenschaften bzgl. ihrer zugrunde liegenden Domain interpretiert werden müssen. Nur so kann sichergestellt werden, dass eine Datenauswertung in relevante Handlungs- und Entscheidungskompetenz Überführt wird. Auf der anderen Seite führt die schiere Menge an verfügbaren Daten ebenfalls zu erschwerten Auswertungsbedingungen, weil weder die Daten in ihrer Gesamtheit gesichtet werden können noch eine geeignete Strategie die Daten in unabhängige Sub-Datenmengen zu zerlegen trivial benannt werden kann.

In dieser Vorlesung diskutieren wir Methoden Daten automatisch zu analysieren, ebenso wie Methoden den Nutzer von Daten in den Analyseprozess mit einzubinden, welches unter dem Stichwort Daten Visualisierung durchaus dem ein oder anderen unter Ihnen bekannt sein dürfte. Eine logische Konsequenz ist es beide Ansätze miteinander zu kombinieren und um interaktive Methoden zu bereichern: Ein Ansatz welcher als Visual Analytics bezeichnet wird und welchen wir ebenfalls in dieser Veranstaltung aufgreifen und diskutieren werden.

Die Veranstaltung selbst ist breit aufgestellt und self-contained. Notwendiges Wissen für das Verständnis von relevanten Methoden wird in den ersten Wochen vorgestellt, um im folgenden Automatische Analyse Methoden, visuelle Methoden, und Methoden der Visual Analytics zu besprechen.
Lecturer:
Dr.-Ing. Dirk Joachim Lehmann
Dates:
Donnerstag, 09:00 bis 11:00, G29-335
Classification:
Vorlesung, 2 SWS, ECTS-Credits: 5

WPF CV;B 4-6
WPF IF;B 4-6
WPF IngIF;B 4-6
WPF WIF;B 4-6
Completion:
Seminararbeit + Schriftliche Prüfung (2 h am Ende des Semesters) (bei einer Teilnehmerzahl weniger als 15 findet eine mündliche Prüfung statt)
Certificate/Schein:
bestandene Seminararbeit + Prüfung
Additional Information:
> Additional Information <

Prüfungstermine

Prüfungen finden in 243 Spezialgerätelabor statt

Mögliche Themen für die Hausarbeit:

Bitte wählt bis zum 09.11.2017 ein Thema für eure Hausarbeit und sendet es mir per Mail zu (dirk@isg.cs.uni-magdeburg.de). Danke.
Hinweis: Nur Einzelarbeiten sind zulässig. Themen werden nicht doppelt vergeben (first come, first serve).

  • Graph-Visualization (z.B. Edge-Bundles, Tree-Maps etc.)
  • Clustering Approaches (z.B. K-Means, Local Outlier Factor, DBScan, Clusterfactors)
  • Machine Learning vs. Data Mining
  • Mathematische Grundlagen für die Visualisierung (Projektionen, Abbildungen, Einbettungen, Transformationen, Matrizen, Statistik, Optimierung, etc.)
  • Bivariate vs. Multivariate Visualization Approaches (z.B. Scatterplots, Parallel Coordinates, RadVis)
  • Globale vs. Lokale Projektionen (z.B. Lamp)
  • Approaches for Focus and Context and Level of Detail in Visual Analytics
  • Quality Metrics in Visual Search
  • Generation of high-dimensional or multivariate synthetic Data
  • Text Visualization
  • Methoden der Rück-Projektion von Merkmalen im Visualisierungsraum auf Strukturen im Datenraum (z.B. iLamp)
  • Merkmalsdetektion in Visualisierungen (Features-Spaces, Ridges, Density Peaks, allgemein Topologie, etc.)
  • Data Touren hochdimensionaler Daten (Grand Tour, PCA Tour, Optimal Set of Projections, Projection Pursuit etc.)
  • Sub-Space Clustering
  • Explorative Datenvisualisierung vs. visuelle Representation von Daten
  • Visualisierung von Medizinischen Daten ODER Biologischen Daten ODER Strömungsdaten
  • Visualization Lies: Fehler in der Analyse von Daten hervorgerufen durch die Visualisierung von Daten
  • Visual Design: Methoden zur Wahl geeigneter Visualisierungen
  • Eigenes Thema

Hausarbeitsthemen:

Abgabe der Hausarbeit als PDF oder Word-Dokument bis spätestens 18.01.2018 elektronisch an dirk@isg.cs.uni-magdeburg.de

Sobald ihr eure Themen gewählt habt, werden diese hier dargestellt, um Dopplungen zu vermeiden.

Hinweise zum Schreiben wissenschaftlicher Arbeiten:

Wenn möglich, sollte für die Erstellung von wiss. Arbeiten ein Textsatzsystem eingesetzt werden. Im Gegensatz zu klassischen WYSIWYG Systemen wie MS Word u.ä., folgen solche Textsatzsysteme dem WYSIWYM-Prinzip (What You See Is What You Mean). Der Umstieg zwischen den Systemen bedarf einer gewissen Eingewöhnungsphase, er lohnt sich dennoch. Gewinnt man doch eine große Menge an Freiheiten bei der Text, Formel und Bildgestaltung; und auch komplexe Dokumente lassen sich effizient erstellen und einfach verwalten.

Das wohl bekannteste professionelle Textsatzsystem ist LaTeX (ursprünglich TeX). Ein geeigneter kostenloser Editor für Tex ist MikTex. Zur Verwaltung von Referenzen kann JabRef eingesetzt und genutzt werden.

Literatur:

[AM1] S. Aeberhard, D. Coomans, O. De Vel
Comparative Analysis of Statistical Pattern Recognition Methods in High-Dimensional Settings
IEEE Signal Processing Workshop on Higher Order Statistics, 1994

[AM2] S. Agarwal, J. Wills, L. Cayton, G. Lanckriet, D. Kriegman, S. Belongie
Generalized Non-metric Multidimensional Scaling
AISTATS, 2007

[AM3] G. Albuquerque, D.J. Lehmann, T. Rodermund, M. Eisemann, T. Nocke, M. Magnor, H. Theisel
Semi-Automatic Classification of Weather Maps
Technical Report 2012-3-17, TU Braunschweig

[AM4] G. Albuquerque, M. Eisemann, D. J. Lehmann, H. Theisel, M. Magnor
Quality-Based Visualization Matrices
Proceedings of Vision, Modeling, and Visualization (VMV 2009)

[AM5] G. Albuquerque, M. Eisemann, D. J. Lehmann, H Theisel, M. Magnor
Improving the Visual Analysis of High-dimensional Datasets Using Quality Measures
Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (IEEE VAST), 2010

[AM6] Ethem Alpaydin
Introduction to Machine Learning
Alpaydin, Cambridge University Press, 2004

[AM7] E.P.S. Amorim, E. Brazil, J. Daniels, P. Joia, L.G. Nonato, M.C. Sousa
iLAMP: Exploring High-Dimensional Spacing through Backward Multidimensional Projection
IEEE Conf. on Vis. Analytics Sci. Tech. (VAST), 2012

[AM8] M. Ankerst, S. Berchtold, D. A. Keim
Similarity Clustering of Dimensions for an Enhanced Visualization of Multidimensional Data
IEEE Computer Society, 1998

[AM9] D. Asimov
The grand tour: a tool for viewing multidimensional data
Journal on Scientific and Statistical Computing, 1985

[AM10] S. Bachthaler, D. Weiskopf
Efficient and Adaptive Rendering of 2-D Continuous Scatterplots
Computer Graphics Forum, 2009

[AM11] R. Becker, W. Cleveland
Brushing scatterplots
Technometrics, 1987

[AM12] B. Benjamin Bederson, A. Clamage, M. P. Czerwinski, G. G. Robertson
DateLens: A Fisheye Calendar Interface for PDAs
ACM Transactions on Computer-Human Interaction, 2004

[AM13] A. Ben-Hur, D. Horn, H. T. Siegelmann, V. Vapnik
Support Vector Clustering
JOURNAL OF MACHINE LEARNING RESEARCH, 2001

[AM14] {R. D. Bergeron, G. Grindstein
A Reference Model for the Visualization of Multidimensional Data
Proc. Eurographics, 1989

[AM15] E. Bertini
Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization
IEEE Computer Society, 2011

[AM16] E. Bertini, A. Di Girolamo, G. Santucci
See What You Know: Analyzing Data Distribution to Improve Density Map Visualization
EuroVis, 2007

[AM17] M. Boisot, A. Canals
Data, information and knowledge: have we got it right?
IN3 Working Paper Series, 2004

[AM18] C. A. Brewer, G. Hatchard, M. Harrower
ColorBrewer in Print: A Catalog of Color Schemes for Maps
Cartography and Geographic Information Science, 2003

[AM19] M. A. Harrower, C. A. Brewer
ColorBrewer.org: An Online Tool for Selecting Color Schemes for Maps
The Cartographic Journal, 2003

[AM20] D. B. Carr, R. J. Littlefield, W. L. Nichloson
Scatterplot matrix techniques for large n
Proceedings of the Seventeenth Symposium on the Interface of Computer Sciences and Statistics on Computer Science and Statistics, 1986

[AM21] Y.-H. Chan, C.D. Correa, K.-A. Ma
Flow-based Scatterplots for Sensitivity Analysis
Proceedings of IEEE VAST Symposium, 2010

[AM22] C. Viau, M. J. McGuffin, Y. Chiricota, I. Jurisica
The FlowVizMenu and Parallel Scatterplot Matrix: Hybrid Multidimensional Visualizations for Network Exploration
IEEE Computer Society, 2010

[AM23] D. Cook, A. Buja, J. Cabreta, C. Hurley
Grand tour and projection pursuit
Journal of Computational and Statistical Computing, 1995

[AM24] K. Crammer, Y. Singer
On the algorithmic implementation of multiclass kernel-based vector machines
J. Mach. Learn. Res., 2002

[AM25] N. Dowson, T. Kadir, R. Bowden
Estimating the Joint Statistics of Images Using Nonparametric Windows with Application to Registration Using Mutual Information
IEEE Trans. Pattern Anal. Mach. Intell., 2008

[AM26] M. Eisemann, G. Albuquerque, M. Magnor
Data Driven Color Mapping
Proc. EuroVA, 2011

[AM27] N. Elmqvist, P. Dragicevic, J.-D. Fekete
Rolling the dice: Multidimensional visual exploration using scatterplot matrix navigation
IEEE Trans. Vis. Comput. Graph., 2008

[AM28] H. Jair Escalante
A Comparison of Outlier Detection Algorithms for Machine Learning
Proceedings of the International Conference on Communications in Computing, 2005

[AM29] J. H. Friedman, J. W. Tukey
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Trans. Comput., 1974

[AM30] Jerome H. Friedman
Exploratory projection pursuit
Journal of the American Statistical Association, 1987

[AM31] H. Garcke, T. Preusser, T., M. Rumpf, A. Telea, U. Weikard, J. van Wijk
A continuous clustering method for vector fields
IEEE Computer Society Press, 2000

[AM32] C. G. Healey
Choosing effective colours for data visualization
IEEE Computer Society Press, 1996

[AM33] J. Heinrich, S. Bachthaler, D. Weiskopf
Progressive Splatting of Continuous Scatterplots and Parallel Coordinates
Computer Graphics Forum, 2011

[AM34] J. Heinrich, D. Weiskopf
Continuous Parallel Coordinates
IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2009)

[AM35] P. Hoffman, G. Grinstein, Georges, K. Marx, Kenneth, I. Grosse, E. Stanley
DNA visual and analytic data mining
IEEE Computer Society Press, 1997

[AM36] P. Hoffman, G Grinstein, D Pinkney
Dimensional anchors: a graphic primitive for multidimensional multivariate information visualizations
NPIVM '99

[AM37] P. J. Huber
{Projection Pursuit
The Annals of Statistics, 1985

[AM38] A. Inselberg
The plane with parallel coordinates
The Visual Computer, 1985

[AM39] Sara Johansson and Jimmy Johansson
Interactive Dimensionality Reduction Through User-defined Combinations of Quality Metrics
IEEE Transactions on Visualization and Computer Graphics, 2009

[AM40] B. Johnson, B. Shneiderman
Tree-Maps: a space-filling approach to the visualization of hierarchical information structures
IEEE Computer Society Press, 1991

[AM41] T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, A. Y. Wu
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Trans. Pattern Anal. Mach. Intell., 2002

[AM42] Daniel A. Keim
Designing Pixel-Oriented Visualization Techniques: Theory and Applications
IEEE Transactions on Visualization and Computer Graphics, 2000

[AM43] D. Keim, H. P. Kriegel, T. Seidl
Visual Feedback in Querying Large Databases
Proc. Visualization IEEE Computer Society Press, 1993

[AM44] D. Keim, M. Ankerst, H.P. Kriegel
Recursive Pattern: A Technique for Visualizing Very Large Amounts of Data
Proc. Visualization 1995 IEEE Computer Society Press, 1995

[AM45] D. A. Keim, Ming C. Hao, U. Dayal, M. Hsu
Pixel Bar Charts: A Visualization Technique for Very Large Multi-Attribute Data Sets
Information Visualization, 2002

[AM46] D. Keim
Information {V}isualization and {V}isual {D}ata {M}ining
In: IEEE Transactions on Visualization and Computer Graphics, 2002

[AM47] T. Kohonen
The self-organizing map
Proceedings of the IEEE, 1990

[AM48] M. Kreuseler, H. Schumann
A Flexible Approach for Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics, 2002

[AM49] A. Kuhn, D. J. Lehmann, R. Gaststeiger, M. Neugebauer, B Preim, H. Theisel
A Clustering-based Visualization Technique to Emphasize Meaningful Regions of Vector Fields
Proc. Vision, Modeling and Visualization (VMV), 2011

[AM50] D. J. Lehmann, H. Theisel
Features in {C}ontinuous {P}arallel {C}oordinates
IEEE Transactions on Visualization and Computer Graphics (Proceedings IEEE Visualization), 2011

[AM51] D. J. Lehmann, H. and Theisel
Discontinuities in {C}ontinuous {S}catterplots
IEEE Transactions on Visualization and Computer Graphics (Proceedings IEEE Visualization), 2010

[AM52] D. J. Lehmann, G. Albuquerque, M. Eisemann, A. Tatu, D. Keim, H. Schumann, M. Magnor, H. Theisel
Visualisierung und {A}nalyse multidimensionaler Datensätze
Informatik-Spektrum, 2010

[AM53] Hao, Fogarty
Cascaded treemaps: examining the visibility and stability of structure in treemaps
Canadian Information Processing Society, 2008

[AM54] T. Munzner, F. Guimbretiere, S. Tasiran, L. Zhang, Y. Zhou
TreeJuxtaposer: Scalable Tree Comparison Using Focus+Context with Guaranteed Visibility.
ACM Transactions on Graphics: Special Issue Proceedings of ACM SIGGRAPH, 2003

[AM55] Nov\'{a}kov\'{a}, Lenka and {S}t {e}p\'{a}nkov\'{a}, Olga
RadViz and Identification of Clusters in Multidimensional Data
IV '09: Proceedings of the 2009 13th International Conference Information Visualisation, 2009

[AM56] Nov\'{a}kov\'{a}, Lenka and {S}t {e}p\'{a}nkov\'{a}, Olga
Visualization of Trends Using RadViz
ISMIS '09: Proceedings of the 18th International Symposium on Foundations of Intelligent Systems, 2009

[AM57] Nov\'{a}kov\'{a}, Lenka and {S}tep\'{a}nkov\'{a}, Olga
Multidimensional clusters in RadViz
World Scientific and Engineering Academy and Society (WSEAS), 2006

[AM58] D. Oelke, D. Spretke, A. Stoffel, D. A. Keim
Visual Readability Analysis: How to make your writings easier to read
Proceedings of IEEE Conference on Visual Analytics Science and Technology (VAST '10), 2010

[AM59] R. M. Picket, G. Grindstein
Iconographics Displays for Visualizing Multidimensional Data.
Proc. IEEE Conference on Systems, Man and Cybernetics, 1988

[AM60] P. Pirolli, R. Rao
Table lens as a tool for making sense of data
AVI '96: {P}roceedings of the {W}orkshop on {A}dvanced {V}isual {I}nterfaces, 1996

[AM61] R. Rao, S. K. Card
The Table Lens: Merging {G}raphical and {S}ymbolic {R}epresentations in an {I}nteractive {F}ocus+{C}ontext {V}isualization for {T}abular {I}nformation.
In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, 1994

[AM62] Rauschenbach, U. and Weinkauf, T. and Schumann, H.
Interactive Focus and Context Display of Large Raster Images
In: Proceedings WSCG: The 8-th International Conference in Central Europe on Computer Graphics, Visualization and Interactive Digital Media, 2000

[AM63] {Schlechtweg, S. and Schulze-Wollgast, P. and Schumann, H.
Interactive Treemaps with Detail on Demand to Support Information Search in Documents}
Proceedings of the Joint Eurographics/IEEE TCVG Symposium on Visualization, 2004

[AM64] H. Schumann and W. Müller
Visualisierung: Grundlagen und allgemeine Methoden
Springer Verlag, 2000

[AM65] Seo, J. and Shneiderman, B
{A rank-by-feature framework for interactive exploration of multidimensional data
Information Visualization, Palgrave Macmillan, 2005

[AM66] Sips, Mike and Neubert, Boris and Lewis, John P. and Hanrahan, Pat
Selecting good views of high-dimensional data using class consistency
_Computer Graphics Forum (Proc. EuroVis 2009), Blackwell Ltd., 2009 _

[AM67] Sharko, John and Grinstein, Georges and Marx, Kenneth A.},
Vectorized Radviz and Its Application to Multiple Cluster Datasets
IEEE Transactions on Visualization and Computer Graphics, 2008

[AM68] Soukup, T. and Davidson, I.
Visual Data Mining: Techniques and Tools for Data Visualization and Mining
John Wiley & Sons, Inc. New York, 2002

[AM69] Stasko, John
An evaluation of space-filling information visualizations for depicting hierarchical structures
Int. J. Hum.-Comput. Stud., 2000

[AM70] Stolte, c and Tang, D. and Hanrahan, P.
Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases
IEEE Transactions on Visualization and Computer Graphics, 2002

[AM71] A. Tatu and G. Albuquerque and M. Eisemann and J. Schneidewind and H. Theisel and M. Magnor and D. Keim
Combining automated analysis and visualization techniques for effective exploration of high-dimensional data
Proceedings of IEEE Symposium on Visual Analytics Science and Technology (IEEE VAST), 2009

[AM72] Tatu, Andrada and Bak, Peter and Bertini, Enrico and Keim, Daniel and Schneidewind, Joern
Visual quality metrics and human perception: an initial study on 2D projections of large multidimensional data
Proceedings of the International Conference on Advanced Visual Interfaces

[AM73] Andrada Tatu and Georgia Albuquerque and Martin Eisemann and Peter Bak and Holger Theisel and Marcus Magnor and Daniel Keim
Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data
IEEE Trans. Visualization and Computer Graphics, 2011

[AM74] Torgerson, Warren
Multidimensional scaling: I. Theory and method
Springer New York,1952

[AM75] J. Tukey and P. Tukey
Computing graphics and exploratory data analysis: An {I}ntroduction.
Proceedings of the Sixth Annual Conference and Exposition: Computer Graphics 85. Nat. Computer Graphics Assoc., 1985

[AM76] Wilkinson, L. and Anand, A. and Grossman, R.
Graph-Theoretic Scagnostics
IEEE Symposium on Information Visualization, 2005

[AM77] Witten, I.H. and Frank, E. and Hall, M.A.
Data Mining: Practical Machine Learning Tools and Techniques
Elsevier Science \& Technology, 2011

[AM78] P. C. Wong and R. D. Bergeron
30 Years of Multidimensional Multivariate Visualization
IEEE Computer Society Press, 1992

[AM79] Yang, J. and Ward, M. and Rundensteiner, E. and Huang, S.
Visual hierarchical dimension reduction for exploration of high dimensional datasets
Proceedings of the Symposium on Data Visualisation , 2003

[AM80] Jing Yang and Wei Peng and Matthew O. Ward and Elke A. Rundensteiner
Interactive Hierarchical Dimension Ordering, Spacing and Filtering for Exploration of High Dimensional Datasets
Proc. IEEE Symposium on Information Visualization, 2003

[AM81] S. Bachthaler, D. Weiskopf
Continuous Scatterplots
IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2008)

[AM82] D. Holten
Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data
IEEE Computer Society, 2006

[AM83] Daniel A. Keim and Florian Mansmann and Jörn Schneidewind and Hartmut Ziegler and Jim Thomas
Visual Analytics: Scope and Challenges
_ Visual Data Mining: Theory, Techniques and Tools for Visual Analytics, Springer, Lecture Notes In Computer Science (lncs), 2008_

[AM84] A. Inselberg, B. Dimsdale
Multidimensional Lines II: Proximity and applications
__ SIAM Journal on Applied Mathematics, 1994

[AM85] J. Schneidewind and M. Sips and D.A. Keim
Pixnostics: Towards Measuring the Value of Visualization}
IEEE Computer Society, 2006

[AM86] dos Santos Amorim, Elisa Portes and Brazil, Emilio Vital and II, Joel Daniels and Joia, Paulo and Nonato, Luis Gustavo and Sousa, Mario Costa
iLAMP: Exploring high-dimensional spacing through backward multidimensional projection
__ IEEE VAST, 2012

[AM87] D. A. Keim.
Designing pixel-oriented visualization techniques: Theory and applications
IEEE Transactions on Visualization and Computer Graphics, 2000

[AM88] G. Albuquerque, M. Eisemann, D. J. Lehmann, H. Theisel, M. Magnor,
Improving the Visual Analysis of High-dimensional Datasets Using Quality Measures
Proc. IEEE Symposium on Visual Analytics Science and Technology (VAST), 2010

[AM89] G. Albuquerque, M. Eisemann, D. J. Lehmann, H. Theisel, M. Magnor,
Quality-Based Visualization Matrices
Proceedings of Vision, Modeling, and Visualization (VMV), 2009

[AM90] G.Albuquerque, T. Löwe, M. Magnor,Synthetic
Generation of High-dimensional Datasets
Proc. IEEE InfoVis, 2011

[AM91] Jürgen Waser, Raphael Fuchs, Hrvoje Ribicic, Benjamin Schindler, Günther Blöschl, and M. Eduard Gröller,
World Lines
IEEE Transactions on Visualization and Computer Graphics(16(6)),2010

[AM92] Mike Sips, Boris Neubert, John P. Lewis, Pat Hanrahan:
Selecting good views of high-dimensional data using class consistency
Computer Graphics Forum (Proc. EuroVis 2009), 2009

[AM93] P. Hoffman, G. Grinstein, K. Marx, I. Grosse, E. Stanley.
DNA visual and analytic data mining
In Proceedings of the 8th conference on Visualization, 1997

[AM94] B. Shneiderman
Treemaps for spaceconstrained visualization of hierarchies
ACM Transactions on Graphics, Jan. 1992

[AM95] Thomas Seidl and Tobias Schreck and Enrico Bertini and Ines Farber and Fabian Maas and Andrada Tatu and Daniel Keim
Subspace Search and Visualization to Make Sense of Alternative Clusterings in High-Dimensional Data
Procedings of IEEE Symposium on Visual Analytics Science and Technology (VAST), 2012

[AM96] E.P.S. Amorim, E. Brazil, J. Daniels, P. Joia, L.G. Nonato, M.C. Sousa
iLAMP: Exploring High-Dimensional Spacing through Backward Multidimensional Projection
IEEE Conf. on Vis. Analytics Sci. Tech. (VAST), 2012

[AM97] Albuquerque, Georgia and Löwe, Thomas and Magnor, Marcus
Synthetic Generation of High-dimensional Datasets
{IEEE} Transactions on Visualization and Computer Graphics {(TVCG,} Proc. Visualization / InfoVis), 2011

[AM98] W. von Funck, T. Weinkauf, H. Theisel, and H.-P. Seidel,
Smoke Surfaces: An Interactive Flow Visualization Technique Inspired by Real-World Flow Experiments
IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE Visualization), vol. 14, no. 6, pp. 1396-1403, Nov. 2008

[AM99] F. Ferstl, K. Burger, H. Theisel, and R. Westermann,
Interactive Separating Streak Surfaces
Visualization and Computer Graphics, IEEE Transactions on, vol. 16, no. 6, pp. 1569—1577, 2010

[AM100] T. Germer, M. Otto, R. Peikert and H. Theisel
Lagrangian Coherent Structures with Guaranteed Material Separation
Computer Graphics Forum (Proc. EuroVis), 2011

[AM101] D. J. Lehmann and H. Theisel
Discontinuities in Continuous Scatterplots
IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE Visualization), 2010

[AM102] J. Heinrich, S. Bachthaler and D. Weiskop
Progressive Splatting of Continuous Scatterplots and Parallel Coordinates
IEEE Symposium on Visualization 2011 (EuroVis 2011), Volume 30 (2011), Number 3, June 2011

[AM103] D. J. Lehmann and H. Theisel
Features in Continuous Parallel Coordinates
IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE Visualization), 2011

[AM104] T. Salzbrunn and G. Scheuermann
Streamline Predicates
IEEE Transactions on Visualization and Computer Graphics, vol. 12, pp. 1601-1612, 2006

[AM105] P. Dobrev, T. Van Long and L. Linsen
A Cluster Hierarchy-based Volume Rendering Approach for Interactive Visual Exploration of Multi-variate Volume Data
Proc. of Vision, Modeling and Visualization Workshop (VMV), Oct. 2011

[AM106] C.-K. Chen, S. Yan, H. Yu, N. Max, and K.-L. Ma
An Illustrative Visualization Framework for 3D Vector Fields
Proc. of Pacific Graphics 2011, Sept. 2011

[AM107] A. Kratz, N. Kettlitz, I. Hotz
Particle-Based Anisotropic Sampling for Two-Dimensional Tensor Field Visualization
Proc. of Vision, Modeling and Visualization Workshop (VMV), Oct. 2011

[AM108] A. Kuhn, D. J. Lehmann, R. Gasteiger, M. Neugebauer, B. Preim, H. Theisel
A Clustering-based Visualization Technique to Emphasize Meaningful Regions of Vector Fields
Proc. of Vision, Modeling and Visualization Workshop (VMV), Oct. 2011

[AM109] George Haller
A variational theory of hyperbolic Lagrangian Coherent Structures
Physica D: Nonlinear Phenomena, 2011

[AM110] A. Kuhn and C. Rössl and T. Weinkauf and H. Theisel
A Benchmark for Evaluating FTLE Computations
Proceedings of 5th IEEE Pacific Visualization Symposium (PacificVis), 2012

[AM111] Turkay, Cagatay and Filzmoser, Peter and Hauser, Helwig
Brushing Dimensions – A Dual Visual Analysis Model for High-Dimensional Data
IEEE Trans. Vis. Comput. Graph., 2011

[AM112] Michael Sedlmair and Tamara Munzner and Melanie Tory
Empirical Guidance on Scatterplot and Dimension Reduction Technique Choices
IEEE Trans. on Visualization and Computer Graphics, 2013

[AM113] E. Kandogan
Star Coordinates: A Multi-Dimensional Visualization Technique with Uniform Treatment of Dimensions
Proc. of the IEEE Information Visualization Symposium, 2000

[AM114] Dirk J. Lehmann and Holger Theisel
Orthographic Star Coordinates
IEEE Trans. on Visualization and Computer Graphics (Proc. IEEE Information Visualization), 2013

[AM115] Alexander Klippel, Frank Hardisty, Chris Weaver
Star Plots: How Shape Characteristics Influence Classification Tasks
Cartography and Geographic Information Science, 2009

[AM116]Theisel, Holger
Higher Order Parallel Coordinates
VMV, 2000

[AM117] Dirk J. Lehmann and Holger Theisel
Optimal Sets of Projections of High-Dimensional
IEEE Transactions on Visualization & Computer Graphics (Proc. IEEE Information Visualization), 2015

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