Particle Swarm Optimization Trained Class Association Rule Mining

Association and classification are two important tasks in data mining. Literature abounds with works that unify these two techniques. This paper presents a new algorithm called Particle Swarm Optimization trained Classification Association Rule Mining (PSOCARM) for associative classification that generates class association rules (CARs) from transactional database by formulating a combinatorial global optimization problem, without having to specify minimal support and confidence unlike other conventional associative classifiers. We devised a new rule pruning scheme in order to reduce the number of rules and increasing the generalization aspect of the classifier. We demonstrated its effectiveness for phishing email and phishing website detection. Our experimental results indicate the superiority of our proposed algorithm with respect to accuracy and the number of rules generated as compared to the state-of-the-art algorithms.