Term Weighting Evaluation in Bipartite Partitioning for Text Clustering

摘要：

To alleviate the problem of high dimensions in text clustering,an alternative to conventional methods is bipartite partitioning,where terms and documents are modeled as vertices on two sides respectively.Term weighting schemes,which assign weights to the edges linking terms and documents,are vital for the final clustering performance.In this paper,we conducted an comprehensive evaluation of six variants of tf/idf factor as term weighting schemes in bipartite partitioning.With various external validation measures,we found tfidf most effective in our experiments.Besides,our experimental results also indicated that df factor generally leads to better performance than tf factor at moderate partitioning size.

作者: Chao Qu Yong Li Jun Zhu Peican Huang Ruifen Yuan Tianming Hu

作者单位: Dongguan University of Technology,China;Zhongshan University,China Dongguan University of Technology,China Dongguan University of Technology,China;East China Normal University,China

会议类型: 国际会议

会议名称: 4th Asia Information Retrieval Symposium(AIRS 2008)(第四届亚洲信息检索研讨会)

会议地点: 哈尔滨

会议语种:英文

页码: 393-400

在线出版日期: 2008-01-16（万方平台首次上网日期，不代表论文的发表时间）