Open AccessA Comprehensive Survey of Frequent Itemsets Mining on Transactional Database with Weighted Items
Author(s) -
Thanh Huan Phan,
Hoài Bắc Lê
Publication year - 2021
Publication title -
research on information comunication technology
Language(s) - English
Resource type - Journals
ISSN - 1859-3534
DOI - 10.32913/mic-ict-research.v2021.n1.967
Subject(s) - transactional leadership , computer science , data mining , database transaction , meaning (existential) , database , transaction data , association rule learning , information retrieval , psychology , social psychology , psychotherapist
In 1993, Agrawal et al. proposed the first algorithm for mining traditional frequent itemset on binarytransactional database with unweighted items - This algorithmis essential in finding hindden relationships among items inyour data. Until 1998, with the development of various typesof transactional database - some researchers have proposed afrequent itemsets mining algorithms on transactional databasewith weighted items (the importance/meaning/value of itemsis different) - It provides more pieces of knowledge thantraditional frequent itemsets mining. In this article, the authors present a survey of frequent itemsets mining algorithmson transactional database with weighted items over the pasttwenty years. This research helps researchers to choose theright technical solution when it comes to scale up in big datamining. Finally, the authors give their recommendations anddirections for their future research.