摘要
提出了一种使用后缀树聚类算法优化K-means文档聚类初始值的快速混合聚类方法STK-means。该方法首先构建文档集的后缀树模型,使用后缀树聚类算法识别初始聚类、提取K-means聚类算法初始值中心值。然后,把后缀树模型的节点映射到M维向量空间模型中的特征项,利用TF-IDF方案计算基于短语的文档向量特征值。最后,使用K-means算法产生聚类结果。实验结果表明该方法优于传统K-means聚类算法和后缀树聚类算法,并具备了这些算法聚类速度快的优点。
A fast hybrid clustering algorithm for Web documents clustering is proposed which optimizes the initial center val- ues of K-means algorithm through STC algorithm.Firstly,the initial center values are extracted after the Web document set is clustered by STC algorithm.Secondly,by mapping the each internal node of suffix tree into M-dimensional VSM,each fea- ture term weights is computed using TF-IDF extended with phrases.Finally, the final result is generated by K-means algo- rithm.The evaluation experiments indicate that the new hybrid algorithm is more effective on clustering documents than ordi- nary K-means and STC algorithm.Moreover,it is as fast as K-means and STC algorithm.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第22期12-15,共4页
Computer Engineering and Applications
基金
国家科技支撑计划No.2007BAH08B04
重庆市科技支撑计划No.2008AC20084~~
