  The process of data mining produces various patterns from a given data
source. The most recognized data mining tasks are the process of discovering
frequent itemsets, frequent sequential patterns, frequent sequential rules and
frequent association rules. Numerous efficient algorithms have been proposed to
do the above processes. Frequent pattern mining has been a focused topic in
data mining research with a good number of references in literature and for
that reason an important progress has been made, varying from performant
algorithms for frequent itemset mining in transaction databases to complex
algorithms, such as sequential pattern mining, structured pattern mining,
correlation mining. Association Rule mining (ARM) is one of the utmost current
data mining techniques designed to group objects together from large databases
aiming to extract the interesting correlation and relation among huge amount of
data. In this article, we provide a brief review and analysis of the current
status of frequent pattern mining and discuss some promising research
directions. Additionally, this paper includes a comparative study between the
performance of the described approaches.
