基于复杂网络社团划分的文本聚类方法  

Text clustering method based on partitioning community in complex network

作　　者：谢凤宏[1] 张大为[1] 黄丹[1] 谢福鼎[1] 

机构地区：[1]辽宁师范大学计算机与信息技术学院,辽宁大连116081

出　　处：《计算机工程与设计》2011年第3期1059-1061,1078,共4页Computer Engineering and Design

基　　金：国家自然科学基金项目(10771092)

摘　　要：文本聚类是文本挖掘的一种重要方法,提出了一种加权复杂网络社团划分的新算法,通过不断寻找复杂网络中的稠密集并对其进行适当操作,达到了划分加权复杂网络的目的。将该算法应用于文本聚类,将文本用向量空间模型表示,用余弦公式计算文本之间的相似度,根据邻居节点构造出加权复杂网络,用提出的算法对加权复杂网络进行社团划分。对Reu-ters-21578数据集中的部分样本进行聚类,实验结果表明了该方法具有良好的聚类效果。Text clustering is an important method for text mining.Firstly,a new algorithm for detecting community structures in a weighted complex network is proposed.To partition the weighted complex network into groups,the algorithm looks for the density sets constantly and some proper operations are executed.Secondly,the proposal is applied to cluster text documents which are represented by using the vector space model.A weighted complex network is constructed in terms of the similarity between two documents calculated by the cosine function.And then the community structure in this network is detected by the proposed algorithm.Finally,the experiment results show that the proposed algorithm has a good clustering efficiency by clustering some samples of Reuters-21578 data sets.

关 键 词：文本聚类 复杂网络 社团结构 稠密集 邻居 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]