基于主动学习的深度半监督聚类模型OA北大核心CSTPCD

Deep semi-supervised clustering aims to achieve better clustering results using a small amount of supervised infor-mation.However,the amount of supervised information is often limited due to the expensive labelling cost.Therefore,with limited supervised information,it becomes crucial to select the most valuable supervisory information for clustering.To address the above problem,this paper proposed a deep active semi-supervised clustering model(DASCM)which designed an active learning method that was able to select marginal texts containing rich information and further generated high-value supervised information containing edge texts.The model used this supervised information to guide the clustering,thus improving the clus-tering performance.The experimental results on five real text datasets show that the clustering performance of DASCM is signi-ficantly improved.This result verifies that supervised information generated using active learning methods that cover marginal text is effective in improving clustering.