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NARD Intelligence est ravie d’annoncer qu’un projet de recherche en collaboration entre des chercheurs du d\u00e9partement Informatique de l’Institut Sup\u00e9rieur des Technologies de l’Information<\/a> (Tunisie) et de la Communication (Tunisie) et du Big Data Institute de l’Universit\u00e9 de Shenzhen<\/a> (Chine) a \u00e9t\u00e9 accept\u00e9 pour publication dans la conf\u00e9rence International Conference on Big Data Analytics (BDA 2022)<\/a> qui se tiendra du 19 au 22 d\u00e9cembre 2022 \u00e0 Hederabad, en Inde.<\/p>\n\n\n\n Liste de tous les papiers accept\u00e9es \u00e0 la conf\u00e9rence : BDA Accepted Papers List<\/a> <\/p>\n\n\n\n Ce travail pr\u00e9sente une nouvelle approche pour l’apprentissage des plongements (embeddings) des tranactions sur la base des ‘Itemsets’ \u00e0 haute valeur utile. Il s’agit d’une adaptation de Word2vec<\/a>, la tr\u00e8s c\u00e9l\u00e8bre technique de Natural Language Processing<\/a> (NLP) qui utilise un mod\u00e8le de r\u00e9seau de neurones pour apprendre les plongements de mots (word embeddings) \u00e0 partir d’un grand corpus de texte, au domaine pattern\/itemset mining.<\/p>\n\n\n\n Mining frequent itemsets (FIs) in transaction databases is a very popular task in data mining. It helps create meaningful and effective representations for customer transactions which is a key step in the process of transaction classification and clustering. To improve the quality of these representations, previous studies have adapted vector embedding methods to learn transaction embeddings from items and FIs. #word-embedding #machine-learning #ml #ai #high-utility #data-mining <\/p>\n","protected":false},"excerpt":{"rendered":"Abstract<\/h4>\n\n\n\n
However, FIs are still a simple pattern type that ignores important information about transactions such as the purchase quantities of items and their unit profits.
To address this issue, we propose to learn transaction embeddings from items and high-utility itemsets (HUIs), a more general pattern type. Since HUIs were shown to be more appropriate than FIs for a wide range of applications, we take for hypothesis that transaction embeddings learned from HUIs will be more representative and meaningful. We introduce an unsupervised method, named Hui2Vec, to learn transaction embeddings by combining both singleton items and HUIs. We demonstrate the superior quality of the embedding achieved with the proposed method compared to the embeddings learned from items and FIs on four datasets.<\/p>\n\n\n\n