摘要
This paper used an efficient hybrid data mining approach, called gSSO proposed by Yeh in 2014 [1], is a modification introduced to simplified swarm optimization and based on K-harmonic means (KHM) algorithm to help the KHM algorithm escape from local optimum. To test its solution quality, the proposed gSSO is compared with other recently introduced KHM-based Algorithms in a larger size dataset named car dataset in the UCI database. The experimental results conclude that the proposed gSSO outperforms other algorithms in the solution quality of all aspects including average, minimum, maximum, and standard deviation for space and stability.