TY  - JOUR
T1  - A Survey on Association Rule Mining Approaches for Malicious Detection
AU - Obeis, Nawfal Turki AU - Bhaya, Wesam 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 21
SP  - 5394
EP  - 5398
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.5394.5398
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.5394.5398
KW  - Association rule
KW  -malicious detection
KW  -frequent pattern mining
KW  -data mining
KW  -survey
KW  -itemsets
AB  - The quality of malicious detector is determined by the technique it uses. The extracting of interest
and useful knowledge from huge data called data mining. It uses with many aspects of clustering, classification,
association rule mining, frequent pattern mining, etc. Association rule mining is a significant technique to finds
interesting relationships among items in various datasets. Recently, association rule discovery has turned to
important topics in data mining with malicious detection. It attracts extracares because of its varied usability.
The association rule mining is normally worked by generating of frequent itemsets and rules in which many
researchers provided many effective algorithms. To discover these rules, it needs to find frequent itemsets.
Based on these frequent itemsets, it can build blocks of association rules with a given support and confidence
factors. Here in this study, a survey on association rule algorithms will be present. At the beginning, we present
the concepts of association rules and some of the related research works which done on it. Then, a discussion
of the limitations and advantages of association rule algorithms will provide.
ER  -