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Nicolas Labroche
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2020 – today
- 2025
- [j11]Elodie Escriva
, Tom Lefrere, Manon Martin, Julien Aligon
Effective data exploration through clustering of local attributive explanations. Inf. Syst. 127: 102464 (2025) - 2023
- [c51]Adam Przybylek
Databases and Information Systems: Contributions from ADBIS 2023 Workshops and Doctoral Consortium. ADBIS (Short Papers) 2023: 293-311 - [c50]Alexandre Chanson, Nicolas Labroche, Patrick Marcel, Willeme Verdeaux:
Pairwise Loss Regularization for Recommendations Explanation. DOLAP 2023: 91-95 - 2022
- [j10]Léo Brunot, Nicolas Canovas, Alexandre Chanson
Preference-based and local post-hoc explanations for recommender systems. Inf. Syst. 108: 102021 (2022) - [j9]Sihem Amer-Yahia, Angela Bonifati, Cécile Favre, Élisa Fromont, Nicolas Labroche, Guy Melançon
Diversity and Inclusion Activities in EGC - A 2022 Report. SIGKDD Explor. 24(1): 52-56 (2022) - [c49]Michael Franklin Mbouopda, Thomas Guyet
Experimental Study of Time Series Forecasting Methods for Groundwater Level Prediction. AALTD@ECML/PKDD 2022: 34-49 - [c48]Alexandre Chanson, Faten El Outa, Nicolas Labroche, Patrick Marcel, Verónika Peralta, Willeme Verdeaux, Lucile Jacquemart:
Generating Personalized Data Narrations from EDA Notebooks. DOLAP 2022: 91-95 - [c47]Alexandre Chanson, Nicolas Labroche, Patrick Marcel, Stefano Rizzi, Vincent T'kindt:
Automatic generation of comparison notebooks for interactive data exploration. EDBT 2022: 2:274-2:284 - [c46]Nicolas Ringuet, Patrick Marcel
Modeling Lifelong Pathway Co-construction. ER 2022: 130-144 - [c45]Fodil Benali, Damien Bodénès, Nicolas Labroche, Cyril de Runz:
MTCopula: Génération de données synthétiques et complexes basées sur les Copules. EGC 2022: 347-354 - [c44]Fodil Benali, Damien Bodénès, Cyril De Runz, Nicolas Labroche:
An enhanced adaptive geometry evolutionary algorithm using stochastic diversity mechanism. GECCO 2022: 476-483 - [c43]Fodil Benali, Damien Bodénès, Cyril de Runz, Nicolas Labroche:
A new reference-based algorithm based on non-euclidean geometry for multi-stakeholder media planning. SAC 2022: 1056-1065 - [i3]Michael Franklin Mbouopda, Thomas Guyet
Experimental study of time series forecasting methods for groundwater level prediction. CoRR abs/2209.13927 (2022) - 2021
- [c42]Alexandre Chanson
A Chain Composite Item Recommender for Lifelong Pathways. DaWaK 2021: 55-66 - [c41]Alexandre Chanson, Nicolas Labroche, Willeme Verdeaux:
Towards Local Post-hoc Recommender Systems Explanations. DOLAP 2021: 41-50 - [c40]Fodil Benali, Damien Bodenes, Nicolas Labroche, Cyril de Runz:
MTCopula: Synthetic Complex Data Generation Using Copula. DOLAP 2021: 51-60 - [c39]Fodil Benali, Damien Bodénès, Cyril De Runz, Nicolas Labroche:
An Enhanced R-NSGA-II For Multiple Brands Advertising Campaign Allocation Problem. ICTAI 2021: 1306-1310 - 2020
- [j8]Adnan El Moussawi, Arnaud Giacometti, Nicolas Labroche, Arnaud Soulet:
MAPK-means: A clustering algorithm with quantitative preferences on attributes. Intell. Data Anal. 24(2): 459-489 (2020) - [c38]Alexandre Chanson, Ben Crulis, Nicolas Labroche, Patrick Marcel, Verónika Peralta, Stefano Rizzi, Panos Vassiliadis:
The Traveling Analyst Problem: Definition and Preliminary Study. DOLAP 2020: 94-98 - [c37]Willeme Verdeaux, Clément Moreau, Nicolas Labroche, Patrick Marcel:
Causality based explanations in multi-stakeholder recommendations. EDBT/ICDT Workshops 2020 - [c36]Krista Drushku, Julien Aligon, Nicolas Labroche, Patrick Marcel, Verónika Peralta:
Recommandations basées sur les centres d'intérêts utilisateurs en Business Intelligence. INFORSID 2020: 151-152 - [i2]Caroline Pasquer, Agata Savary, Jean-Yves Antoine, Carlos Ramisch, Nicolas Labroche, Arnaud Giacometti:
To Be or Not To Be a Verbal Multiword Expression: A Quest for Discriminating Features. CoRR abs/2007.11381 (2020)
2010 – 2019
- 2019
- [j7]Mahfoud Djedaini, Krista Drushku, Nicolas Labroche, Patrick Marcel
Automatic assessment of interactive OLAP explorations. Inf. Syst. 82: 148-163 (2019) - [j6]Krista Drushku, Julien Aligon
Interest-based recommendations for business intelligence users. Inf. Syst. 86: 79-93 (2019) - [c35]Alexandre Chanson, Ben Crulis, Krista Drushku, Nicolas Labroche, Patrick Marcel:
Profiling User Belief in BI Exploration for Measuring Subjective Interestingness. DOLAP 2019 - [c34]Patrick Marcel, Nicolas Labroche, Panos Vassiliadis:
Towards a Benefit-based Optimizer for Interactive Data Analysis. DOLAP 2019 - [i1]Alexandre Chanson, Ben Crulis, Nicolas Labroche, Patrick Marcel:
A Subjective Interestingness measure for Business Intelligence explorations. CoRR abs/1907.06946 (2019) - 2018
- [c33]Violaine Antoine, Kévin Gravouil
On Evidential Clustering with Partial Supervision. BELIEF 2018: 14-21 - [c32]Martina Megasari, Pandu Wicaksono, Chiao Yun Li, Clément Chaussade, Shibo Cheng, Nicolas Labroche, Patrick Marcel, Verónika Peralta:
Can Models Learned from a Dataset Reflect Acquisition of Procedural Knowledge? An Experiment with Automatic Measurement of Online Review Quality. DOLAP 2018 - [c31]Violaine Antoine, Nicolas Labroche:
Semi-supervised Fuzzy c-Means Variants: A Study on Noisy Label Supervision. IPMU (2) 2018: 51-62 - 2017
- [j5]Viet-Vu Vu
Active seed selection for constrained clustering. Intell. Data Anal. 21(3): 537-552 (2017) - [c30]Mahfoud Djedaini, Nicolas Labroche, Patrick Marcel
Detecting User Focus in OLAP Analyses. ADBIS 2017: 105-119 - [c29]Krista Drushku, Julien Aligon
User Interests Clustering in Business Intelligence Interactions. CAiSE 2017: 144-158 - [c28]Mahfoud Djedaini, Nicolas Labroche, Patrick Marcel, Verónika Peralta:
A benchmark for assessing OLAP exploration assistants. EDA 2017: 81-84 - [c27]Adnan El Moussawi, Philippe De Guis, Arnaud Giacometti, Nicolas Labroche, Arnaud Soulet:
Prototype de clustering exploratoire pour l'aide à la segmentation des clients. EGC 2017: 457-460 - 2016
- [c26]Adnan El Moussawi, Ahmed Cheriat, Arnaud Giacometti, Nicolas Labroche, Arnaud Soulet:
Clustering par apprentissage de distance guidé par des préférences sur les attributs. EGC 2016: 333-344 - [c25]Adnan El Moussawi, Ahmed Cheriat, Arnaud Giacometti, Nicolas Labroche, Arnaud Soulet:
Clustering with Quantitative User Preferences on Attributes. ICTAI 2016: 383-387 - [c24]Mahfoud Djedaini, Pedro Furtado
Benchmarking Exploratory OLAP. TPCTC 2016: 61-77 - 2015
- [j4]Sahar Changuel, Nicolas Labroche
Resources Sequencing Using Automatic Prerequisite-Outcome Annotation. ACM Trans. Intell. Syst. Technol. 6(1): 6:1-6:30 (2015) - [c23]Pedro Furtado
Materializing Baseline Views for Deviation Detection Exploratory OLAP. DaWaK 2015: 243-254 - [c22]Violaine Antoine, Nicolas Labroche:
Classification évidentielle avec contraintes d'étiquettes. EGC 2015: 125-136 - 2014
- [j3]Nicolas Labroche
Online fuzzy medoid based clustering algorithms. Neurocomputing 126: 141-150 (2014) - [c21]Nicolas Labroche, Marcin Detyniecki, Thomas Bärecke:
Comparaison de bornes théoriques pour l'accélération du clustering incrémental en une passe. EGC 2014: 467-478 - [c20]Violaine Antoine, Nicolas Labroche
Evidential seed-based semi-supervised clustering. SCIS&ISIS 2014: 706-711 - 2013
- [c19]Sylvain Dormieu, Nicolas Labroche:
SNOW, un algorithme exploratoire pour le subspace clustering. EGC 2013: 79-84 - [c18]Nicolas Labroche
Accelerating One-Pass Clustering by Cluster Selection Racing. ICTAI 2013: 491-498 - 2012
- [b2]Nicolas Labroche:
Méthodes d'apprentissage automatique pour l'analyse des interactions utilisateurs. (Machine learning methods for the analysis of user interactions). Pierre and Marie Curie University, Paris, France, 2012 - [j2]Viet-Vu Vu
Improving constrained clustering with active query selection. Pattern Recognit. 45(4): 1749-1758 (2012) - [c17]Sahar Changuel, Nicolas Labroche
Content Independent Metadata Production as a Machine Learning Problem. MLDM 2012: 306-320 - 2011
- [c16]Sahar Changuel, Nicolas Labroche
Distinguishing defined concepts from prerequisite concepts in learning resources. CIDM 2011: 22-29 - 2010
- [c15]Viet-Vu Vu, Nicolas Labroche, Bernadette Bouchon-Meunier:
Boosting Clustering by Active Constraint Selection. ECAI 2010: 297-302 - [c14]Viet-Vu Vu
An Efficient Active Constraint Selection Algorithm for Clustering. ICPR 2010: 2969-2972 - [c13]Viet-Vu Vu
Active Learning for Semi-Supervised K-Means Clustering. ICTAI (1) 2010: 12-15 - [c12]Sahar Changuel, Nicolas Labroche
Automatic concept type identification from learning resources. IJCNN 2010: 1-6 - [c11]Nicolas Labroche
Optimization of a fuzzy decision trees forest with artificial ant based clustering. SoCPaR 2010: 1-5
2000 – 2009
- 2009
- [c10]Sahar Changuel, Nicolas Labroche
Automatic Web Pages Author Extraction. FQAS 2009: 300-311 - [c9]Sahar Changuel, Nicolas Labroche
A General Learning Method for Automatic Title Extraction from HTML Pages. MLDM 2009: 704-718 - [c8]Viet-Vu Vu
Leader Ant Clustering with Constraints. RIVF 2009: 1-8 - 2008
- [j1]Marie-Jeanne Lesot, Nicolas Labroche
Analyse et visualisation interactive de sessions web. Rev. d'Intelligence Artif. 22(3-4): 369-382 (2008) - 2007
- [c7]Nicolas Labroche
A New Web Usage Mining and Visualization Tool. ICTAI (1) 2007: 321-328 - 2005
- [c6]Nicolas Labroche:
Mesure d'audience sur Internet par populations de fourmis artificielles. EGC (Ateliers) 2005: 119-124 - 2004
- [c5]Nicolas Labroche
Fast Unsupervised Clustering with Artificial Ants. PPSN 2004: 1143-1152 - 2003
- [b1]Nicolas Labroche:
Modélisation du système de reconnaissance chimique des fourmis pour le problème de la classification non-supervisée : application à la mesure d'audience sur Internet. (Modelling of the chemical recognition system of ants for the unsupervised classification problem : application to web usage mining). François Rabelais University, Tours, France, 2003 - [c4]Nicolas Labroche, Nicolas Monmarché, Gilles Venturini:
AntClust: Ant Clustering and Web Usage Mining. GECCO 2003: 25-36 - [c3]Nicolas Labroche, Nicolas Monmarché, Gilles Venturini:
Visual Clustering with Artificial Ants Colonies. KES 2003: 332-338 - [c2]Nicolas Labroche, Nicolas Monmarché, Gilles Venturini:
Web Sessions Clustering with Artificial Ants Colonies. WWW (Posters) 2003 - 2002
- [c1]Nicolas Labroche, Nicolas Monmarché, Gilles Venturini:
A new clustering algorithm based on the ants chemical recognition system. ECAI 2002: 345-349
Coauthor Index
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