A Comprehensive Survey of Frequent Itemsets Mining on Transactional Database with Weighted Items

Abstract

In 1993, Agrawal et al. proposed the first algorithm for mining traditional frequent itemset on binary
transactional database with unweighted items - This algorithm
is essential in finding hindden relationships among items in
your data. Until 1998, with the development of various types
of transactional database - some researchers have proposed a
frequent itemsets mining algorithms on transactional database
with weighted items (the importance/meaning/value of items
is different) - It provides more pieces of knowledge than
traditional frequent itemsets mining. In this article, the authors present a survey of frequent itemsets mining algorithms
on transactional database with weighted items over the past
twenty years. This research helps researchers to choose the
right technical solution when it comes to scale up in big data
mining. Finally, the authors give their recommendations and
directions for their future research.