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ORCIDMondher Maddouri
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2020 – today
- 2022
[j15]Wissem Inoubli
, Sabeur Aridhi, Haithem Mezni, Mondher Maddouri, Engelbert Mephu Nguifo:
A distributed and incremental algorithm for large-scale graph clustering. Future Gener. Comput. Syst. 134: 334-347 (2022)- 2021
- [j14]Nida Meddouri, Hela Khoufi, Mondher Maddouri:
DFC: A Performant Dagging Approach of Classification Based on Formal Concept. Int. J. Artif. Intell. Mach. Learn. 11(2): 38-62 (2021) - 2020
- [j13]Nida Meddouri, Mondher Maddouri:
Efficient Closure Operators for FCA-Based Classification. Int. J. Artif. Intell. Mach. Learn. 10(2): 79-98 (2020) - [j12]Manel Zoghlami, Sabeur Aridhi, Mondher Maddouri, Engelbert Mephu Nguifo:
Multiple instance learning for sequence data with across bag dependencies. Int. J. Mach. Learn. Cybern. 11(3): 629-642 (2020) - [c31]Wissem Inoubli, Sabeur Aridhi, Haithem Mezni, Mondher Maddouri, Engelbert Mephu Nguifo:
Un algorithme distribué pour le clustering de grands graphes. EGC 2020: 349-356 - [c30]Hayfa Azibi, Nida Meddouri, Mondher Maddouri:
Catégorisation des méthodes de classification fondées sur l'Analyse de Concepts Formels. IC 2020: 159-162 - [c29]Hayfa Azibi, Nida Meddouri, Mondher Maddouri:
Survey on Formal Concept Analysis Based Supervised Classification Techniques. MLIS 2020: 21-29
2010 – 2019
- 2019
- [j11]Rabie Saidi, Wajdi Dhifli, Mondher Maddouri, Engelbert Mephu Nguifo:
Efficiently Mining Recurrent Substructures from Protein Three-Dimensional Structure Graphs. J. Comput. Biol. 26(6): 561-571 (2019) - [c28]Rawia Fray, Nida Meddouri, Mondher Maddouri:
Cloud Implementation of Classier Nominal Concepts using DistributedWekaSpark. ICFCA (Supplements) 2019: 125-136 - [c27]Manel Zoghlami, Sabeur Aridhi, Mondher Maddouri, Engelbert Mephu Nguifo:
A Structure Based Multiple Instance Learning Approach for Bacterial Ionizing Radiation Resistance Prediction. KES 2019: 342-351 - 2018
- [j10]Wissem Inoubli, Sabeur Aridhi, Haithem Mezni, Mondher Maddouri, Engelbert Mephu Nguifo:
An experimental survey on big data frameworks. Future Gener. Comput. Syst. 86: 546-564 (2018) - [c26]Manel Zoghlami, Sabeur Aridhi, Mondher Maddouri, Engelbert Mephu Nguifo:
ABClass : Une approche d'apprentissage multi-instances pour les séquences(ABClass: A multiple instance learning approach for sequence data). CNIA+RJCIA 2018: 10-18 - [c25]Wissem Inoubli, Sabeur Aridhi, Haithem Mezni, Mondher Maddouri, Engelbert Mephu Nguifo:
A Comparative Study on Streaming Frameworks for Big Data. LADaS@VLDB 2018: 17-24 - 2017
- [c24]Marwa Trabelsi, Nida Meddouri, Mondher Maddouri:
A New Feature Selection Method for Nominal Classifier based on Formal Concept Analysis. KES 2017: 186-194 - 2016
- [j9]Sabeur Aridhi, Haïtham Sghaier, Manel Zoghlami, Mondher Maddouri, Engelbert Mephu Nguifo:
Prediction of Ionizing Radiation Resistance in Bacteria Using a Multiple Instance Learning Model. J. Comput. Biol. 23(1): 10-20 (2016) - [c23]Marwa Trabelsi, Nida Meddouri, Mondher Maddouri:
New Taxonomy of Classification Methods Based on Formal Concepts Analysis. FCA4AI@ECAI 2016: 113-120 - [i1]Manel Zoghlami, Sabeur Aridhi, Haïtham Sghaier, Mondher Maddouri, Engelbert Mephu Nguifo:
A multiple instance learning approach for sequence data with across bag dependencies. CoRR abs/1602.00163 (2016) - 2015
- [j8]Sabeur Aridhi, Laurent d'Orazio, Mondher Maddouri, Engelbert Mephu Nguifo:
Density-based data partitioning strategy to approximate large-scale subgraph mining. Inf. Syst. 48: 213-223 (2015) - [c22]Sabeur Aridhi, Laurent d'Orazio, Mondher Maddouri, Engelbert Mephu Nguifo:
Cost Models for Distributed Pattern Mining in the Cloud. TrustCom/BigDataSE/ISPA (2) 2015: 112-119 - 2014
- [j7]Sabeur Aridhi, Laurent d'Orazio, Mondher Maddouri, Engelbert Mephu Nguifo:
Un partitionnement basé sur la densité de graphe pour approcher la fouille distribuée de sous-graphes fréquents. Tech. Sci. Informatiques 33(9-10): 711-737 (2014) - [c21]Nida Meddouri, Hela Khoufi, Mondher Maddouri:
Parallel Learning and Classification for Rules based on Formal Concepts. KES 2014: 358-367 - 2013
- [c20]Sabeur Aridhi, Mondher Maddouri, Haïtham Sghaier, Engelbert Mephu Nguifo:
Computational phenotype prediction of ionizing-radiation-resistant bacteria with a multiple-instance learning model. BIOKDD 2013: 18-24 - 2012
- [c19]Rabie Saidi, Sabeur Aridhi, Engelbert Mephu Nguifo, Mondher Maddouri:
Feature extraction in protein sequences classification: a new stability measure. BCB 2012: 683-689 - [c18]Nida Meddouri, Hela Khoufi, Mondher Maddouri:
Diversity Analysis on Boosting Nominal Concepts. PAKDD (1) 2012: 306-317 - 2011
- [c17]Nida Meddouri, Hela Khoufi, Mondher Maddouri:
Vers un critère d'arrêt de Boosting basé sur la diversité des classifieurs. INFORSID 2011: 111-126 - 2010
- [j6]Rabie Saidi, Mondher Maddouri, Engelbert Mephu Nguifo:
Protein sequences classification by means of feature extraction with substitution matrices. BMC Bioinform. 11: 175 (2010) - [c16]Besma Khalfi, Rahma Cherif, Nida Meddouri, Mondher Maddouri:
Développement de méthodes de classification basées sur l'analyse de concepts formels sous la plateforme WEKA. EGC 2010: 621-622 - [c15]Nida Meddouri, Mondher Maddouri:
Apprentissage supervisé adaptatif de Concepts Formels à partir des données nominales. EGC 2010: 649-650 - [c14]Rabie Saidi, Sabeur Aridhi, Mondher Maddouri, Engelbert Mephu Nguifo:
Etude de stabilité de méthodes d'extraction de motifs à partir des séquences protéiques. EGC 2010: 703-704 - [c13]Nida Meddouri, Mondher Maddouri:
Adaptive Learning of Nominal Concepts for Supervised Classification. KES (1) 2010: 121-130
2000 – 2009
- 2009
- [c12]Nida Meddouri, Mondher Maddouri:
Générer des règles de classification par dopage de concepts formels. EGC 2009: 181-186 - [c11]Nida Meddouri, Mondher Maddouri:
Boosting Formal Concepts to Discover Classification Rules. IEA/AIE 2009: 501-510 - [c10]Rabie Saidi, Mondher Maddouri, Engelbert Mephu Nguifo:
Comparing graph-based representations of protein for mining purposes. KDD Workshop on Statistical and Relational Learning in Bioinformatics 2009: 35-38 - [p1]Emna Bahri, Stéphane Lallich, Nicolas Nicoloyannis, Mondher Maddouri:
A Hybrid Approach of Boosting Against Noisy Data. Mining Complex Data 2009: 41-54 - 2008
- [c9]Emna Bahri, Mondher Maddouri:
Une nouvelle approche du Boosting face aux données réelles. EGC 2008: 349-360 - 2007
- [c8]Rabie Saidi, Mondher Maddouri, Engelbert Mephu Nguifo:
Biological Sequences Encoding for Supervised Classification. BIRD 2007: 224-238 - [c7]Emna Bahri, Nicolas Nicoloyannis, Mondher Maddouri:
Improving Boosting by Exploiting Former Assumptions. MCD 2007: 131-142 - [c6]Rabie Saidi, Mondher Maddouri, Engelbert Mephu Nguifo:
Classification supervisée de séquences biologiques basée sur les motifs et les matrices de substitution. EGC 2007: 409-420 - [c5]Mondher Maddouri, Jamil Gammoudi:
On Semantic Properties of Interestingness Measures for Extracting Rules from Data. ICANNGA (1) 2007: 148-158 - 2006
- [c4]Mondher Maddouri, Faouzi Mhamdi:
Construction d'attributs pour l'extraction de connaissances à partir de séquences biologiques. AAFD 2006: 107-134 - [c3]Mondher Maddouri, Fatma Kaâbi:
On Statistical Measures for Selecting Pertinent Formal Concepts to Discover Production Rules from Data. ICDM Workshops 2006: 780-784 - 2005
- [j5]Mourad Elloumi, Mondher Maddouri:
New voting strategies designed for the classification of nucleic sequences. Knowl. Inf. Syst. 8(1): 1-15 (2005) - [c2]Mondher Maddouri:
A Formal Concept Analysis Approach to Discover Association Rules from Data. CLA 2005 - 2004
- [j4]Mondher Maddouri:
Towards a machine learning approach based on incremental concept formation. Intell. Data Anal. 8(3): 267-280 (2004) - [j3]Mondher Maddouri, Mourad Elloumi:
Encoding of Primary Structures of Biological Macromolecules Within a Data Mining Perspective. J. Comput. Sci. Technol. 19(1): 78-88 (2004) - 2002
- [j2]Mondher Maddouri, Mourad Elloumi:
A data mining approach based on machine learning techniques to classify biological sequences. Knowl. Based Syst. 15(4): 217-223 (2002)
1990 – 1999
- 1998
- [j1]Mondher Maddouri, Samir Elloumi, Ali Jaoua:
An Incrementa Learning System for Imprecise and Uncertain Knowledge Discovery. Inf. Sci. 109(1-4): 149-164 (1998) - 1996
- [c1]Mondher Maddouri, Ali Jaoua:
Incremental Rule Production: Towards a Uniform Approach for Knowledge Organization. IEA/AIE 1996: 295-304
Coauthor Index
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