An improved rule mining technology based on swarm intelligence computation

Zhaoyin Zhang 

COMPUTER MODELLING & NEW TECHNOLOGIES 2014 18(12A) 137-142                                                                                     

School of Computer Science and Technology in Heilongjiang University 150010, China

The traditional artificial fish swarm algorithm is easy to converge to local optimum. An improved artificial fish swarm algorithm is proposed which modifies position update formula of fish according to acceleration. Then rule mining algorithm based on improved artificial fish swarm is proposed, which includes rule coding, rule evaluation and determination of fitness function. UCI data set is used to test the performance of proposed algorithm. The experiment results show that the proposed algorithm has higher classification accuracy than particle swarm optimization and artificial fish swarm algorithm. It also has fast convergence speed compared with traditional artificial fish swarm algorithm.