A particle swarm optimization based simultaneous learning framework for clustering and classification

Ruochen Liu, Yangyang Chen, Licheng Jiao, Yangyang Li

Abstract: Highlights•A particle swarm optimization based simultaneous learning framework for clustering and classification (PSOSLCC) is proposed.•The simultaneous frame consists of two parts: clustering section and classification section.•An automatic clustering algorithm is used to find the proper number of clusters.•An improved particle swarm optimization with a global factor is used in the training phase.•The performance of PSOSLCC has been extensively compared with four state-of-the-art classification algorithms over a test suit of datasets and texture image segmentation.