Nicolas Labroche

519 total citations
15 papers, 92 citations indexed

About

Nicolas Labroche is a scholar working on Artificial Intelligence, Information Systems and Signal Processing. According to data from OpenAlex, Nicolas Labroche has authored 15 papers receiving a total of 92 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 6 papers in Information Systems and 5 papers in Signal Processing. Recurrent topics in Nicolas Labroche's work include Advanced Clustering Algorithms Research (5 papers), Data Mining Algorithms and Applications (4 papers) and Explainable Artificial Intelligence (XAI) (3 papers). Nicolas Labroche is often cited by papers focused on Advanced Clustering Algorithms Research (5 papers), Data Mining Algorithms and Applications (4 papers) and Explainable Artificial Intelligence (XAI) (3 papers). Nicolas Labroche collaborates with scholars based in France and Vietnam. Nicolas Labroche's co-authors include Bernadette Bouchon‐Meunier, Gilles Venturini, Patrick Marcel, Julien Aligon, Arnaud Soulet, Arnaud Giacometti, Nicolas Monmarché, Vincent t'Kindt, Cyrille Delpierre and Louis Casteilla and has published in prestigious journals such as Pattern Recognition, Neurocomputing and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

Nicolas Labroche

11 papers receiving 84 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nicolas Labroche France 6 74 29 18 16 11 15 92
David Gross-Amblard France 4 69 0.9× 81 2.8× 24 1.3× 20 1.3× 19 1.7× 16 115
Karen Pinel-Sauvagnat France 6 86 1.2× 37 1.3× 51 2.8× 38 2.4× 30 2.7× 35 127
Yanshuai Cao Canada 6 107 1.4× 39 1.3× 16 0.9× 17 1.1× 2 0.2× 13 135
Guillaume Cleuziou France 6 84 1.1× 27 0.9× 15 0.8× 22 1.4× 8 0.7× 11 101
S. Goldberg United States 6 68 0.9× 11 0.4× 16 0.9× 10 0.6× 8 0.7× 9 85
Shuichi Katsumata Japan 7 109 1.5× 20 0.7× 30 1.7× 17 1.1× 22 2.0× 15 123
Chris Schwiegelshohn Denmark 5 49 0.7× 25 0.9× 9 0.5× 13 0.8× 20 1.8× 19 77
Daniel Rausch Germany 7 60 0.8× 12 0.4× 58 3.2× 12 0.8× 32 2.9× 11 102
Yonglin Hao China 5 103 1.4× 67 2.3× 12 0.7× 7 0.4× 11 1.0× 17 124

Countries citing papers authored by Nicolas Labroche

Since Specialization
Citations

This map shows the geographic impact of Nicolas Labroche'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 Nicolas Labroche with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicolas Labroche more than expected).

Fields of papers citing papers by Nicolas Labroche

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nicolas Labroche. 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 Nicolas Labroche. The network helps show where Nicolas Labroche may publish in the future.

Co-authorship network of co-authors of Nicolas Labroche

This figure shows the co-authorship network connecting the top 25 collaborators of Nicolas Labroche. A scholar is included among the top collaborators of Nicolas Labroche 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 Nicolas Labroche. Nicolas Labroche is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
1.
Aligon, Julien, Nicolas Labroche, Cyrille Delpierre, et al.. (2025). Discernibility in explanations: Designing more acceptable and meaningful machine learning models for medicine. Computational and Structural Biotechnology Journal. 27. 1800–1808.
2.
Martin, Manon, et al.. (2024). Effective data exploration through clustering of local attributive explanations. Information Systems. 127. 102464–102464.
3.
Labroche, Nicolas, et al.. (2024). Comparison Queries Generation Using Mathematical Programming for Exploratory Data Analysis. IEEE Transactions on Knowledge and Data Engineering. 36(12). 7792–7804.
4.
Runz, Cyril de, et al.. (2022). An enhanced adaptive geometry evolutionary algorithm using stochastic diversity mechanism. Proceedings of the Genetic and Evolutionary Computation Conference. 476–483.
5.
Labroche, Nicolas, et al.. (2022). Preference-based and local post-hoc explanations for recommender systems. Information Systems. 108. 102021–102021. 4 indexed citations
6.
Runz, Cyril de, et al.. (2022). A new reference-based algorithm based on non-euclidean geometry for multi-stakeholder media planning. Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing. 1056–1065. 1 indexed citations
7.
Giacometti, Arnaud, et al.. (2020). MAPK-means: A clustering algorithm with quantitative preferences on attributes. Intelligent Data Analysis. 24(2). 459–489. 4 indexed citations
8.
Aligon, Julien, et al.. (2018). Interest-based recommendations for business intelligence users. Information Systems. 86. 79–93. 11 indexed citations
9.
Labroche, Nicolas, et al.. (2017). Active seed selection for constrained clustering. Intelligent Data Analysis. 21(3). 537–552. 8 indexed citations
10.
Labroche, Nicolas, et al.. (2015). Resources Sequencing Using Automatic Prerequisite--Outcome Annotation. ACM Transactions on Intelligent Systems and Technology. 6(1). 1–30. 14 indexed citations
11.
Labroche, Nicolas. (2013). Online fuzzy medoid based clustering algorithms. Neurocomputing. 126. 141–150. 10 indexed citations
12.
Labroche, Nicolas, et al.. (2011). Improving constrained clustering with active query selection. Pattern Recognition. 45(4). 1749–1758. 31 indexed citations
13.
Labroche, Nicolas, et al.. (2010). Automatic concept type identification from learning resources. HAL (Le Centre pour la Communication Scientifique Directe). 13. 1–6. 2 indexed citations
14.
Labroche, Nicolas & Gilles Venturini. (2003). Web sessions clustering with artificial ants colonies. 5 indexed citations
15.
Labroche, Nicolas, Nicolas Monmarché, & Gilles Venturini. (2002). A new clustering algorithm based on the ants chemical recognition system.. European Conference on Artificial Intelligence. 345–349. 2 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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