摘要
提出了一种新的Web文本聚类算法WTCA——基于自组织特征映射神经网络(SOM)的聚类算法。该算法分为训练SOM网络及聚类分析两个阶段,具有自稳定性,无须外界给出评价函数;能够识别概念空间中最有意义的特征,抗噪音能力强。该算法应用到现代远程教育网,可以对各类远程教育站点上收集的文本资料信息自动进行聚类分析;从海量Web文本信息源中快速有效地获取重要的知识。
In this paper,we present a new algorithm of Web text clustering mining WTCA.This algorithm includes the training stage and the clustering stage of SOM network.It can distinguish the most meaningful features from the Concept Space without the evaluation function.The algorithm has been applied to the Modern Long-distance Education Net.It can automatically congregate the text information of education field,which is collected from education sites and help people to browse the important information quickly by information navigation mechanism and acquire useful knowledge.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第4期170-172,共3页
Computer Engineering and Applications
基金
北京市自然科学基金(the Natural Science Foundation of Beijing City of China under Grant No.4022008)。
