New Discrete Crow Search Algorithm for Class Association Rule Mining

Associative classification (AC) or class association rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, the authors present a new and improved discrete version of the crow search algorithm (CSA) called NDCSA-CAR to mine the class association rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on 11 benchmark datasets and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.