Authors : Nawfal Turki Obeis and Wesam Bhaya
Abstract: 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.
Nawfal Turki Obeis and Wesam Bhaya, 2017. A Survey on Association Rule Mining Approaches for Malicious Detection. Journal of Engineering and Applied Sciences, 12: 5394-5398.