A Novel Text Clustering Method Based on DSOM-FS-FCM

摘要：

Because of some problems existing in text clustering such as the high-dimensional sparse text data,poor efficiency of unsupervised feature selection,and defects existing in classical clustering methods and so on,a novel text clustering flow model (TCFM) and a effective text clustering approach called DSOM-FS-FCM according to TCFM are proposed.DSOM-FS-FCM fully combines the dynamic self-organizing maps (DSOM) neural network,features selection (FS) and fuzzy C-means (FCM) clustering.Experimental results indicate that DSOM-FS-FCM clustering outperforms traditional clustering methods such as K-means,and the precision is better than DSOM-FCM and DSOM clustering.

关键词： dynamic self-organizing map neural network text clustering Fuzzy C-means clustering

作者: Jinzhu Hu Chun Fang Bin He Cong Zhang Dongmeng Zhao Yi Zhang

作者单位: Department of Soft Engineering and Information System,Center China of Normal University,Wuhan,Hubei 430079,China

会议类型: 国际会议

会议名称: 2008年国际电子商务、工程及科学领域的分布式计算和应用学术研讨会(2008 International Symposium on Distributed Computing and Applications for Business Engineering and Science)

会议地点: 大连

会议语种:英文

页码: 354-359

在线出版日期: 2008-07-27（万方平台首次上网日期，不代表论文的发表时间）