1 Division of Data Science, Ton Duc Thang University, Ho Chi Minh, Viet Nam 4 2 Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh, Viet Nam 5 [email protected], [email protected] 6 7 Abstract: In this paper, a method for mining frequent weighed closed itemsets (FWCIs) 8 from weighted item transaction databases is proposed. The motivation for FWCIs is that 9 frequent ...

Mining frequently appearing patterns in a database is a basic problem in informatics, especially in data mining. Particularly, when the input database is a collection of subsets of an itemset, the problem is called the frequent itemset mining problem, and has been extensively studied. In the real-world use, one of difficulties of frequent itemset mining is that real-world data is often incorrec...

Frequent itemset counting is the first step for most association rule algorithms and some classification algorithms. It is the process of counting the number of occurrences of a set of items that happen across many transactions. The goal is to find those items which occur together most often. Expressing this functionality in RDBMS engines is difficult for two reasons. First, it leads to extreme...

While frequent pattern mining is fundamental for many data mining tasks, mining maximal frequent itemsets efficiently is important in both theory and applications of frequent itemset mining. The fundamental challenge is how to search a large space of item combinations. Most of the existing methods search an enumeration tree of item combinations in a depthfirst manner. In this thesis, we develop...

Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cro...