Semi-supervised PSO clustering algorithm based on self-adaptive parameter optimisation. (2018) - Pan, Xiuqin; Zhou, Wenmin; Lu, Yong; Sun, Dongyin
Particle swarm optimisation (PSO) based on semi-supervised learning (SSPSO) is known for its higher clustering accuracy than other classical clustering algorithms. However, a fixed parameter representing the use ratio of labelled sample and unlabelled sample for the clustering is selected. Consequently, the determination method of this parameter makes the clustering result difficult to reach the best one. In this paper, we propose an improved clustering algorithm to solve parameter optimisation problem for PSO based on semi-supervised learning. The new approach is called APO_SSPSO, which employs an adaptive strategy based on PSO to dynamically adjust the usage ratio of labelled and unlabelled samples for the clustering. Experiments are conducted on two sets of test samples. Simulation results show that the proposed algorithm is effective and valid.