Martin Theus

753 total citations
16 papers, 380 citations indexed

About

Martin Theus is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Martin Theus has authored 16 papers receiving a total of 380 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Vision and Pattern Recognition, 7 papers in Artificial Intelligence and 3 papers in Signal Processing. Recurrent topics in Martin Theus's work include Data Visualization and Analytics (7 papers), Data Analysis with R (6 papers) and Statistics Education and Methodologies (2 papers). Martin Theus is often cited by papers focused on Data Visualization and Analytics (7 papers), Data Analysis with R (6 papers) and Statistics Education and Methodologies (2 papers). Martin Theus collaborates with scholars based in Germany and United States. Martin Theus's co-authors include Simon Urbanek, Matthias Schonlau, Antony Unwin and Heike Hofmann and has published in prestigious journals such as Journal of Statistical Software, Journal of Computational and Graphical Statistics and Statistics and Computing.

In The Last Decade

Martin Theus

16 papers receiving 332 citations

Peers

Martin Theus

AI

CVPR

SP

CNC

Andreas Wierse Germany

Heidi Lam Canada

Alper Sarıkaya United States

Lucas Vendramin Brazil

Stephen Garner New Zealand

Jörn Schneidewind Germany

Otto Opitz Germany

Nadia Boukhelifa France

Başak Alper United States

Nicholas Kong United States

Andreas Wierse Germany

Martin Theus

112 ×0.7

AI

141 ×0.8

CVPR

72 ×0.9

SP

9 ×0.1

SP

36 ×0.6

CNC

Citations per year, relative to Martin Theus Martin Theus (= 1×) peers Andreas Wierse

Countries citing papers authored by Martin Theus

Since Specialization

Citations

This map shows the geographic impact of Martin Theus's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Martin Theus with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martin Theus more than expected).

Fields of papers citing papers by Martin Theus

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Martin Theus. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Martin Theus. The network helps show where Martin Theus may publish in the future.

Co-authorship network of co-authors of Martin Theus

This figure shows the co-authorship network connecting the top 25 collaborators of Martin Theus. A scholar is included among the top collaborators of Martin Theus based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Martin Theus. Martin Theus is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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16 of 16 papers shown

1.

Theus, Martin. (2012). Mosaic plots. Wiley Interdisciplinary Reviews Computational Statistics. 4(2). 191–198. 4 indexed citations

2.

Theus, Martin. (2010). Mondrian. Wiley Interdisciplinary Reviews Computational Statistics. 2(5). 606–612. 2 indexed citations

3.

Theus, Martin & Simon Urbanek. (2008). Interactive Graphics for Data Analysis: Principles and Examples (Computer Science and Data Analysis). 21 indexed citations

4.

Theus, Martin & Simon Urbanek. (2008). Interactive Graphics for Data Analysis : Principles and Examples. CERN Document Server (European Organization for Nuclear Research). 45 indexed citations

5.

Theus, Martin & Simon Urbanek. (2008). Interactive Graphics for Data Analysis. 33 indexed citations

6.

Unwin, Antony, Martin Theus, & Heike Hofmann. (2006). Graphics of Large Datasets: Visualizing a Million (Statistics and Computing). Springer eBooks. 13 indexed citations

7.

Unwin, Antony, Heike Hofmann, & Martin Theus. (2006). Graphics of Large Datasets: Visualizing a Million. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 49 indexed citations

8.

Unwin, Antony, Martin Theus, & Heike Hofmann. (2006). Graphics of Large Datasets. Statistics and Computing. 11 indexed citations

9.

Unwin, Antony & Martin Theus. (2004). Papers on data visualisation—What can we see in them?. Computational Statistics. 19(1). 5–8. 1 indexed citations

10.

Theus, Martin. (2002). Data visualization for domain exploration: highly multivariate interaction techniques. Oxford University Press eBooks. 232–241. 1 indexed citations

11.

Theus, Martin. (2002). Interactive Data Visualization UsingMondrian. Journal of Statistical Software. 7(11). 70 indexed citations

12.

Theus, Martin. (2000). COMPSTAT 1998 — Proceedings in Computational Statistics. Computational Statistics. 15(2). 303–305. 37 indexed citations

13.

Schonlau, Matthias & Martin Theus. (2000). Detecting masquerades in intrusion detection based on unpopular commands. Information Processing Letters. 76(1-2). 33–38. 56 indexed citations

14.

Theus, Martin, et al.. (1999). Visualizing Loglinear Models. Journal of Computational and Graphical Statistics. 8(3). 396–412. 21 indexed citations

15.

Theus, Martin, et al.. (1999). Visualizing Loglinear Models. Journal of Computational and Graphical Statistics. 8(3). 396–396. 15 indexed citations

16.

Theus, Martin. (1999). Analysing storm data using highly interactive tools. Computational Statistics. 14(1). 91–108. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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