Artificial bee colony data miner (ABC-Miner)

Abstract

Data mining aims to discover interesting, non-trivial, and meaningful information from large datasets. One of the data mining tasks is classification, which aims to assign the given datasets to the most suitable classes. Classification rules are used in many domains such as medical sciences, banking, and meteorology. However, discovering classification rules is challenging due to large size and noisy structure of the datasets, and the difficulty of discovering general and meaningful rules. In the literature, there are several classical and heuristic algorithms proposed to mine classification rules out of large datasets. In this paper, a new and novel heuristic classification data mining approach based on artificial bee colony algorithm (ABC) was proposed (ABC-Miner). The proposed approach was compared with Particle Swarm Optimization (PSO) rule classification algorithm and C4.5 algorithm using benchmark datasets. The experimental results show the efficiency of the proposed method.