微型电脑应用2026，Vol.42Issue(7)：11-15,5.
基于ST-DBSCAN算法的网络信息分类集成
Network Information Classification Integration Based on ST-DBSCAN Algorithm
摘要
Abstract
In order to discover valuable information patterns from the vast and constantly changing network data,a network in-formation classification integration method based on spatial-temporal density-based spatial cluster of application with noise(ST-DBSCAN)algorithm is proposed.The relevant concepts of ST-DBSCAN algorithm are redefined to accurately capture the in-herent structure and distribution characteristics of data in the spatiotemporal dimension.Cluster processing is achieved by trav-ersing information points,checking spatiotemporal core points,constructing new clusters and identifying boundary points,and parameters are determined by drawing a spatiotemporal distance frequency histogram.After clustering,multiple spatially and temporally closely related clusters are obtained.To optimize the classification performance,ST-DBSCAN is combined with XG-Boost.By leveraging the supervised learning capability of XGBoost,a network information classification integration model is constructed through the Stacking integration learning strategy to achieve precise prediction of unknown information categories.The second-order Taylor expansion is used to optimize the loss function,improving the accuracy and efficiency of network in-formation classification intergation.The experimental results show that the proposed method has high clustering quality,clear information differentiation between clusters,noise distribution outside clusters,and the F1 value reachs about 0.96.The net-work information classification integration effect is good.关键词
时空密度聚类/网络信息/信息分类集成/时空邻域/时空核心点/XGBoostKey words
ST-DBSCAN/network information/information classification integration/spatiotemporal neighborhood/spatio-temporal core point/XGBoost分类
信息技术与安全科学引用本文复制引用
熊劲磊,付晓坤..基于ST-DBSCAN算法的网络信息分类集成[J].微型电脑应用,2026,42(7):11-15,5.基金项目
武汉市研发科技项目(2020010900) （2020010900）
