A Review of Anomalies Detection Based on Association Rules Techniques

The primary challenge of anomaly detection often result in reducing high false positive rates. Most studies present different data mining techniques to handle this issue. Association rules is one of these techniques, discovers frequent patterns, correlations, associations, or causal structures among huge number of objects or items in transaction databases, relational databases, and other information repositories. This paper presents a comprehensive review of the literature on the techniques of association rules, in works of the last ten years. The findings of this review show that the most used technique was the well-known Apriori algorithm combined with fuzzy logic.