基于文本挖掘的自动非负矩阵分解的层次聚类方法  

Text mining based automatic non-negative matrix factorization of the hierarchical clustering method

作　　者：张文硕[1] 许艳春[1] 谢术芳[1] Zhang Wenshuo;Xu Yanchun;Xie Shufang(Institute of Information Engineering and Art Design,Shandong Kai wen Collegeof Science and Technology,Jinan 250200.China)

机构地区：[1]山东凯文科技职业学院信息工程与艺术设计学院,山东济南250200

出　　处：《江苏科技信息》2019年第4期41-43,共3页Jiangsu Science and Technology Information

摘　　要：随着网络信息技术的飞速发展,人们在如此庞大的信息中如何找到有用的信息成为一个问题,文本挖掘技术在这样的背景下应运而生。为解决层次关系的文字资料的文本挖掘,文章提出一种新自动非负矩阵分解的层次聚类方法。实验结果对实际数据集进行了比较,结果表明,该方法对于所有情况的平均估计要优于其他传统方法,对于具有层次关系的文字资料的数据挖掘是一种较好的方法。With the rapid development of network information technology,how to find useful information in such huge information becomes a problem.Under the background,text mining technology emerged.In order to solve text mining of the hierarchical text information,this paper proposed a new automatic non-negative matrix factorization hierarchical clustering method.Experimental results on real data sets were compared,the results show that the method for all instances of the average estimation is better than other traditional methods.For hierarchical text data mining it is a kind of better method.

关 键 词：非负矩阵分解 层次聚类方法 关联规则 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]