Frequent itemset mining (FIM) is an essential task within data analysis since it is responsible
for extracting frequently occurring events, patterns, or items in data. Insights from such …

As we are living in the era of big data, huge volumes of a wide variety of complex data-which
can be of different levels of veracity-are generated or collected at a high velocity from rich …

Frequent Itemset Mining (FIM) is a fundamental procedure in various data mining techniques
such as association rule mining. Among many existing algorithms, FP-Growth is considered …

Association Rule Mining (ARM) is one of the well know and most researched technique of
data mining. There are so many ARM algorithms have been designed that their counting is a …

Frequent Itemset mining is time consuming for large datasets. Many parallel frequent itemset
mining algorithms have been proposed to speed up the mining process. This paper presents …

Data mining is a set of methods used to mine hidden information from data. It mainly
includes frequent pattern mining, sequential pattern mining, classification, and clustering …

Frequent itemset (FI) mining is an interesting data mining task. Instead of directly mining the
FIs from data it is preferred to mine only the closed frequent itemsets (CFIs) first and then …

High utility pattern mining (HUPM) has become a key issue in knowledge discovery since it
provides retailers and managers with useful information for making decisions efficiently …

Frequent itemset (FI) mining is an interesting data mining task. Directly mining the FIs from
data often requires lots of time and memory, and should be avoided in many cases. A more …

Abstract Frequent Itemsets Mining (FIM) is a fundamental mining model and plays an
important role in Data Mining. It has a vast range of application fields and can be employed …