Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : CERI Systèmes Numériques – IMT Nord Europe
Durée : 5 mois
Contact : jerry.lonlac@imt-nord-europe.fr
Date limite de publication : 2022-03-31

Contexte :
Frequent Itemset Mining (FIM, for short) is an active part of data mining field and an important first step in data analysis. In the last decades, FIM has been applied in a broad range of applications such as e-commerce [4], e-learning [10], malware detection [3]. The application of FIM in a wide range of domains with a proliferation of different type of data has inspired the development of numerous other pattern-mining techniques. Recently, gradual itemsets [6, 2, 11, 7, 9] have then been proposed for analysing numerical data and different algorithms have been designed to automatically extract gradual itemsets from different data model [12, 13, 1, 5]. Gradual itemsets aroused great interest for extracting frequent complex co-variations between numerical attributes in a multitude of areas. However, in some real- world applications, data are subject to noise and measurement error. To date, the effect of noise on classical frequent gradual itemset mining algorithms has been not addressed.

Sujet :
The goal of this work is to propose a noise tolerant gradual itemset model, which unlike classical gradual itemsets [2, 8] tolerates a controlled fraction of errors on the extent of the gradual itemset. By allowing noise, the proposed models will generalize the level-wise enu- meration of different forms of frequent gradual itemsets [2, 12, 8, 7] that can be extracted from different types of complex numerical data but obscured by noise.

References
[1] Aymeric Cˆome and Jerry Lonlac. Extracting frequent (closed) seasonal gradual patterns using closed itemset mining. In IEEE International Conference on Tools with Artificial Intelligence, ICTAI, pages 1442–1448, 2021.
[2] Lisa Di-Jorio, Anne Laurent, and Maguelonne Teisseire. Mining frequent gradual item- sets from large databases. In IDA, pages 297–308, 2009.
[3] Yiheng Duan, Xiao Fu, Bin Luo, Ziqi Wang, Jin Shi, and Xiaojiang Du. Detective: Automatically identify and analyze malware processes in forensic scenarios via dlls. In ICC, pages 5691–5696, 2015.
[4] Philippe Fournier-Viger, Jerry Chun-Wei Lin, Bay Vo, Tin Chi Truong, Ji Zhang, and Hoai Bac Le. A survey of itemset mining. Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 7(4), 2017.
[5] Amel Hidouri, Sa ̈ıd Jabbour, Jerry Lonlac, and Badran Raddaoui. A constraint-based approach for enumerating gradual itemsets. In IEEE International Conference on Tools with Artificial Intelligence, ICTAI, pages 582–589, 2021.
[6] Eyke Hu ̈llermeier. Association rules for expressing gradual dependencies. In PKDD, pages 200–211, 2002.
[7] Jerry Lonlac, Arnaud Doniec, Marin Lujak, and St ́ephane Lecoeuche. Mining frequent seasonal gradual patterns. In Big Data Analytics and Knowledge Discovery – DaWaK, volume 12393, pages 197–207, 2020.
[8] Jerry Lonlac, Yannick Miras, Aude Beauger, Vincent Mazenod, Jean-Luc Peiry, and Engelbert Mephu Nguifo. An approach for extracting frequent (closed) gradual patterns under temporal constraint. In FUZZ-IEEE, pages 878–885, 2018.
[9] Jerry Lonlac and Engelbert Mephu Nguifo. A novel algorithm for searching frequent gradual patterns from an ordered data set. Intell. Data Anal., 24(5):1029–1042, 2020.
[10] Esp ́erance Mwamikazi, Philippe Fournier-Viger, Chadia Moghrabi, and Robert Bau- douin. A dynamic questionnaire to further reduce questions in learning style assess- ment. In Lazaros Iliadis, Ilias Maglogiannis, and Harris Papadopoulos, editors, Artificial Intelligence Applications and Innovations, pages 224–235, 2014.
[11] Benjamin N ́egrevergne, Alexandre Termier, Marie-Christine Rousset, and Jean-Franc ̧ois M ́ehaut. Para miner: a generic pattern mining algorithm for multi-core architectures. DMKD, 28(3):593–633, 2014.
[12] NhatHai Phan, Dino Ienco, Donato Malerba, Pascal Poncelet, and Maguelonne Teis- seire. Mining multi-relational gradual patterns. In SDM, pages 846–854, 2015.
[13] Faaiz Shah, Arnaud Castelltort, and Anne Laurent. Extracting fuzzy gradual patterns from property graphs. In FUZZ-IEEE, pages 1–6, 2019.

Profil du candidat :
– 2nd year student of a Master’s or Engineering of Computer Science degree.
– Goods skills in Artificial Intelligence, more particularly in pattern mining.
– Goods skills in programming language (C++, Python).

Formation et compétences requises :
– 2nd year student of a Master’s or Engineering of Computer Science degree.
– Goods skills in Artificial Intelligence, more particularly in pattern mining.
– Goods skills in programming language (C++, Python).

Adresse d’emploi :
IMT Nord Europe
941, rue Charles Bourseul
CS 10838
59508 DOUAI Cedex – France

Document attaché : 202202041800_Proposal_for_internship_IMT_Nord_Europe_2022.pdf