计算机应用研究2024，Vol.41Issue(10)：2955-2961,7.DOI:10.19734/j.issn.1001-3695.2024.01.0025
基于主动学习的深度半监督聚类模型
Deep active semi-supervised clustering model
摘要
Abstract
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.关键词
深度半监督聚类/主动学习/边缘文本Key words
deep semi-supervised clustering/active learning/marginal text分类
计算机与自动化引用本文复制引用
付艳艳,黄瑞章,薛菁菁,任丽娜,陈艳平,林川..基于主动学习的深度半监督聚类模型[J].计算机应用研究,2024,41(10):2955-2961,7.基金项目
国家自然科学基金资助项目(62066007) （62066007）
贵州省科技支撑计划资助项目(黔科合支撑[2022]一般277) （黔科合支撑[2022]一般277）
