Semi-supervised PSO clustering algorithm based on self-adaptive parameter optimisation Online publication date: Tue, 04-Dec-2018
by Xiuqin Pan; Wenmin Zhou; Yong Lu; Dongyin Sun
International Journal of High Performance Computing and Networking (IJHPCN), Vol. 12, No. 4, 2018
Abstract: 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.
Online publication date: Tue, 04-Dec-2018
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of High Performance Computing and Networking (IJHPCN):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org