More Web Proxy on the site http://driver.im/

article

VizAssist: an interactive user assistant for visual data mining

Authors:

Abdelheq Guettala,

Gilles VenturiniAuthors Info & Claims

The Visual Computer: International Journal of Computer Graphics, Volume 32, Issue 11

Pages 1447 - 1463

https://doi.org/10.1007/s00371-015-1132-9

Published: 01 November 2016 Publication History

Abstract

We study in this work how a user can be guided to find a relevant visualization in the context of visual data mining. We present a state of the art on the user assistance in visual and interactive methods. We propose a user assistant called VizAssist, which aims at improving the existing approaches along three directions: it uses simpler computational models of the visualizations and the visual perception guidelines, in order to facilitate the integration of new visualizations and the definition of a mapping heuristic. VizAssist allows the user to provide feedback in a visual and interactive way, with the aim of improving the data to visualization mapping. This step is performed with an interactive genetic algorithm. Finally, VizAssist aims at proposing a free on-line tool (www.vizassist.fr) that respects the privacy of the user data. This assistant can be viewed as a global interface between the user and some of the many visualizations that are implemented with D3js.

References

[1]

Albuquerque, G., Eisemann, M., Magnor, M.: Perception-based visual quality measures. In: 2011 IEEE conference on visual analytics science and technology (VAST), pp. 13---20 (2011)

[2]

Azzag, H., Picarougne, F., Guinot, C., Venturini, G.: Vrminer: a tool for multimedia database mining with virtual reality. In: Processing and Managing Complex Data for Decision Support, pp. 318---339. (2005)

[3]

Bertini, E., Tatu, A., Keim, D.: Quality metrics in high-dimensional data visualization: an overview and systematization. IEEE Trans. Vis. Comput. Graph. 17(12), 2203---2212 (2011)

Digital Library

[4]

Blake, C.L., Merz, C.J.: UCI repository of machine learning databases. UCI repository of machine learning databases (1998). http://archive.ics.uci.edu/ml/. http://www.ics.uci.edu/mlearn/MLRepository.html

[5]

Bostock, M., Ogievetsky, V., Heer, J.: D3 data-driven documents. IEEE Trans. Vis. Comput. Graph. 17(12), 2301---2309 (2011)

Digital Library

[6]

Boudjeloud, L., Poulet, F.: Visual interactive evolutionary algorithm for high dimensional data clustering and outlier detection. In: Ho, T.B., Cheung, D.W.L., Liu, H. (eds.) PAKDD, Lecture Notes in Computer Science, vol. 3518, pp. 426---431. Springer, Berlin (2005)

[7]

Cancino, W., Boukhelifa, N., Lutton, E.: Evographdice: Interactive evolution for visual analytics. In: IEEE proceedings of the 2012 IEEE congress on evolutionary computation, pp. 2286---2293 (2012)

[8]

Casner, S.M.: Task-analytic approach to the automated design of graphic presentations. ACM Trans. Graph. (TOG) 10(2), 111---151 (1991)

[9]

Cleveland, W.S., McGill, R.: Graphical perception: theory, experimentation, and application to the development of graphical methods. J. Am. Stat. Assoc. 79, 531---554 (1984)

[10]

Dang, T.N., Wilkinson, L.: Scagexplorer: exploring scatterplots by their scagnostics. In: Pacific visualization symposium (PacificVis), 2014 IEEE, pp. 73---80 (2014)

[11]

Dasgupta, A., Kosara, R.: Pargnostics: screen-space metrics for parallel coordinates. IEEE Trans. Vis. Comput. Graph. 16(6), 1017---1026 (2010)

Digital Library

[12]

Dawkins, R.: The Blind Watchmaker. Norton, San Mateo (1986)

[13]

Demiralp, C., Scheidegger, C.E., Kindlmann, G.L., Laidlaw, D.H., Heer, J.: Visual embedding: a model for visualization. IEEE Comput. Graph. Appl. 34(1), 10---15 (2014)

[14]

Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Magz. 17(3), 37 (1996)

[15]

Gnanamgari, S.: Information presentation through default displays. Ph.D. thesis, Philadelphia, PA, USA (1981)

[16]

Goldberg, D.E.: Real-coded genetic algorithms, virtual alphabets, and blocking. Complex Syst. 5(5), 139---167 (1991)

[17]

Gotz, D., Wen, Z.: Behavior-driven visualization recommendation. In: Proceedings of the 14th international conference on intelligent user interfaces. IUI '09, pp. 315---324. ACM, New York, NY, USA (2009)

Digital Library

[18]

Grammel, L., Tory, M., Storey, M.: How information visualization novices construct visualizations. IEEE Trans. Vis. Comput. Graph. 16(6), 943---952 (2010)

Digital Library

[19]

Guettala, A.E.T., Bouali, F., Guinot, C., Venturini, G.: A user assistant for the selection and parameterization of the visualizations in visual data mining. 16th international conference on information visualisation, pp. 252---257 (2012)

[20]

Hand, D.J., Mannila, H., Smyth, P.: Principles of Data Mining. MIT press, Cambridge (2001)

[21]

Hanrahan, P., Stolte, C., Mackinlay, J.: Visual analysis for everyone: understanding data exploration and visualization. In: Tableau Software Inc., p. 27 (2007)

[22]

Healey, C., Kocherlakota, S., Rao, V., Mehta, R., St Amant, R.: Visual perception and mixed-initiative interaction for assisted visualization design. IEEE Trans. Vis. Comput. Graph. 14(2), 396---411 (2008)

Digital Library

[23]

Healey, C.G., Amant, R.S., Elhaddad, M.S.: Via: A perceptual visualization assistant. In: In 28th workshop on advanced imagery pattern recognition (AIPR-99), pp. 2---11 (1999)

[24]

Heer, J., van Ham, F., Carpendale, S., Weaver, C., Isenberg, P.: Creation and collaboration: engaging new audiences for information visualization. In: Kerren, A., Stasko, J.T., Fekete, J.-D. North, C. (eds.) Information Visualization vol. 4950, pp. 92---133. Springer Berlin, Heidelberg (2008)

[25]

Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1975)

[26]

Inselberg, A.: The plane with parallel coordinates. Vis. Comput. 1, 69---91 (1985)

Digital Library

[27]

Key, A., Howe, B., Perry, D., Aragon, C.: Vizdeck: self-organizing dashboards for visual analytics. In: Proceedings of the 2012 ACM SIGMOD International conference on management of data. SIGMOD '12, pp. 681---684. ACM, New York, NY, USA (2012)

Digital Library

[28]

Kim, H.S., Cho, S.B.: Application of interactive genetic algorithm to fashion design. Eng. Appl. Artif. Intell. 13(6), 635---644 (2000)

[29]

Lange, S., Schumann, H., Mller, W., Krmker, D.: Problem-oriented visualisation of multi-dimensional data sets. pp. 1---15. Singapore : World Scientific, c1995. (1995)

[30]

Mackinlay, J.: Automating the design of graphical presentations of relational information. ACM Trans. Graph. 5(2), 110---141 (1986)

Digital Library

[31]

Mackinlay, J.D., Hanrahan, P., Stolte, C.: Show me: automatic presentation for visual analysis. IEEE Trans. Vis. Comput. Graph. 13(6), 1137---1144 (2007)

Digital Library

[32]

Maguire, E., Rocca-Serra, P., Sansone, S.A., Davies, J., Chen, M.: Taxonomy-based glyph design--with a case study on visualizing workflows of biological experiments. IEEE Trans. Vis. Comput. Graph. 18(12), 2603---2612 (2012)

Digital Library

[33]

Pineo, D., Ware, C.: Data visualization optimization via computational modeling of perception. IEEE Trans. Vis. Comput. Graph. 18(2), 309---320 (2012)

Digital Library

[34]

Roth, S.F., Kolojejchick, J., Mattis, J., Goldstein, J.: Interactive graphic design using automatic presentation knowledge. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp. 112---117. ACM (1994)

Digital Library

[35]

Schneidewind, J., Sips, M., Keim, D.A.: Pixnostics: Towards measuring the value of visualization. In: Visual Analytics Science And Technology, 2006 IEEE Symposium On, pp. 199---206. IEEE (2006)

[36]

Schulz, H.J., Nocke, T., Heitzler, M., Schumann, H.: A design space of visualization tasks. IEEE Trans. Vis. Comput. Graph. 19(12), 2366---2375 (2013)

Digital Library

[37]

Senay, H., Ignatius, E.: Vista: a knowledge based system for scientific data visualization. Tech. rep. (1992)

[38]

Senay, H., Ignatius, E.: A knowledge-based system for visualization design. IEEE Comput. Graph. Appl. 14(6), 36---47 (1994)

Digital Library

[39]

Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of the 1996 IEEE symposium on visual languages, VL '96, pp. 336---343. IEEE Computer Society (1996)

Digital Library

[40]

Siegel, S., Castellan, N.: Non-parametric Statistics for the Behavioural Sciences. Mc Graw-Hill book company, New York (1988)

[41]

Smith, J.R.: Designing biomorphs with an interactive genetic algorithm. In: Proceedings of the fourth international conference on genetic algorithms, pp. 535---538 (1991)

[42]

Syswerda, G.: Uniform crossover in genetic algorithms. In: Proceedings of the 3rd international conference on genetic algorithms, pp. 2---9. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1989). http://dl.acm.org/citation.cfm?id=645512.657265

Digital Library

[43]

Tokui, N., Iba, H.: Music composition with interactive evolutionary computation. Communication 17(2), 215---226 (2000)

[44]

Venturini, G., Slimane, M., Morin, F., Asselin de Beauville, J.P.: On using interactive genetic algorithms for knowledge discovery in databases. In: Back, T. (ed.) Genetic Algorithms: Proceedings of the Seventh International Conference, pp. 696---703. Morgan Kaufmann, Michigan State University, East Lansing, MI, USA (1987)

[45]

Verbeke, G., Molenberghs, G.: Linear Mixed Models in Practice: A SAS-Oriented Approach. Springer, New York (1997)

[46]

Voigt, M., Pietschmann, S., Grammel, L., Meißner, K.: Context-aware recommendation of visualization components. In: eKNOW 2012, the fourth international conference on information, process, and knowledge management, pp. 101---109 (2012)

[47]

van Wijk, J.J.: Model-based visualization-computing perceptually optimal visualizations. In: Mathematical foundations of scientific visualization, computer graphics, and massive data exploration, pp. 343---350. Springer, Berlin (2009)

[48]

Wright, A.: Genetic algorithms for real parameter optimization. Found. Genet. Algorithms 1, 205---218 (1991)

[49]

Yi, J.S., Ah Kang, Y., Stasko, J.T., Jacko, J.A.: oward a deeper understanding of the role of interaction in information visualization. IEEE Trans. Vis. Comput. Graph. 13(6), 1224---1231 (2007)

Digital Library

Cited By

Wyss AMorgenshtern GHirsch-Hüsler ABernard J(2024)DaedalusData: Exploration, Knowledge Externalization and Labeling of Particles in Medical Manufacturing — A Design StudyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345632931:1(54-64)Online publication date: 23-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456329
Ceneda DCollins CEl-Assady MMiksch STominski CArleo A(2024)A Heuristic Approach for Dual Expert/End-User Evaluation of Guidance in Visual AnalyticsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332715230:1(997-1007)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327152
Wambecke JLogre IMurgue A(2023)Catalogue Visu: a Tool for Fast Visualization PrototypingProceedings of the 34th Conference on l'Interaction Humain-Machine10.1145/3583961.3583969(1-10)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1145/3583961.3583969
Show More Cited By

VizAssist: an interactive user assistant for visual data mining
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
2. Human-centered computing

Recommendations

A User Assistant for the Selection and Parameterization of the Visualizations in Visual Data Mining
IV '12: Proceedings of the 2012 16th International Conference on Information Visualisation

We deal in this paper with the problem of automating the process of choosing an appropriate visualization and its parameters in the context of visual data mining (VDM). To solve this problem, we develop a user assistant that performs 2 steps: the system ...
PaintingClass: interactive construction, visualization and exploration of decision trees
KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining

Decision trees are commonly used for classification. We propose to use decision trees not just for classification but also for the wider purpose of knowledge discovery, because visualizing the decision tree can reveal much valuable information in the ...
FpViz: a visualizer for frequent pattern mining
VAKD '09: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration

Over the past 15 years, numerous algorithms have been proposed for frequent pattern mining as it plays an essential role in many knowledge discovery and data mining (KDD) tasks. Most of these frequent pattern mining algorithms return the mined results ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image The Visual Computer: International Journal of Computer Graphics

The Visual Computer: International Journal of Computer Graphics Volume 32, Issue 11

November 2016

138 pages

ISSN:0178-2789

Issue’s Table of Contents

Copyright © Copyright © 2016 Springer-Verlag Berlin Heidelberg.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 November 2016

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 29 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wyss AMorgenshtern GHirsch-Hüsler ABernard J(2024)DaedalusData: Exploration, Knowledge Externalization and Labeling of Particles in Medical Manufacturing — A Design StudyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345632931:1(54-64)Online publication date: 23-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456329
Ceneda DCollins CEl-Assady MMiksch STominski CArleo A(2024)A Heuristic Approach for Dual Expert/End-User Evaluation of Guidance in Visual AnalyticsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332715230:1(997-1007)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327152
Wambecke JLogre IMurgue A(2023)Catalogue Visu: a Tool for Fast Visualization PrototypingProceedings of the 34th Conference on l'Interaction Humain-Machine10.1145/3583961.3583969(1-10)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1145/3583961.3583969
Koptelov MZimmermann ABoizumault PBureau RLamotte J(2023)Explanations for Itemset Mining by Constraint Programming: A Case Study Using ChEMBL DataAdvances in Intelligent Data Analysis XXI10.1007/978-3-031-30047-9_17(208-221)Online publication date: 12-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-30047-9_17
Rodrigues ABarbosa GLopes HBarbosa SSantos CBento Villela MRios da Hora Rodrigues Kde Gois Ribeiro Darin T(2022)Investigating whether people identify how suitable a data visualization is for answering specific analysis questionsProceedings of the 21st Brazilian Symposium on Human Factors in Computing Systems10.1145/3554364.3560904(1-11)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3554364.3560904
Wu AWang YZhou MHe XZhang HQu HZhang D(2022)MultiVision: Designing Analytical Dashboards with Deep Learning Based RecommendationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311482628:1(162-172)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TVCG.2021.3114826
Jia SLi ZChen NZhang J(2022)Towards Visual Explainable Active Learning for Zero-Shot ClassificationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311479328:1(791-801)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TVCG.2021.3114793
Srinivasan ASetlur V(2021)Snowy: Recommending Utterances for Conversational Visual AnalysisThe 34th Annual ACM Symposium on User Interface Software and Technology10.1145/3472749.3474792(864-880)Online publication date: 10-Oct-2021
https://dl.acm.org/doi/10.1145/3472749.3474792
Moral Cde Antonio AFerre XRamirez J(2021)A proposed UML-based common model for information visualization systemsMultimedia Tools and Applications10.1007/s11042-020-10306-980:8(12541-12579)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s11042-020-10306-9
Gao QHe ZJing YZhang KWang X(2021)VizGRank: A Context-Aware Visualization Recommendation Method Based on Inherent Relations Between VisualizationsDatabase Systems for Advanced Applications10.1007/978-3-030-73200-4_16(244-261)Online publication date: 11-Apr-2021
https://dl.acm.org/doi/10.1007/978-3-030-73200-4_16
Show More Cited By

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents