摘要： 文本聚类是聚类的一个重要研究分支，是聚类方法在文本处理领域的应用。该文探讨了基于向量空间模型的文本聚类方法，提出了一种文本聚类的改进算法——LP算法。同时，基于语料库的实际聚类效果，就维度确定、特征选择等方面提出优化方案。实验证明，LP算法有效地减少了聚类所消耗的时间，实用性和灵活性都较高。

关键词: 向量空间模型, 文本聚类, 语料库

Abstract: Text clustering, one of the most important research braches of clustering, is the application of clustering algorithm in text processing. This paper discusses different Vector Space Model(VSM)-based clustering algorithms and presents an improved text clustering algorithm——Level-Panel(LP) algorithm. In addition, according to the effects of clustering for the corpus, it presents optimizations of clustering algorithm, including dimension determining, feature selection, etc. It is proved that LP algorithm can effectively reduce the time spending in clustering process. It is high in practicability and flexibility.

Key words: Vector Space Model(VSM), text clustering, corpus

中图分类号: 

• TP301.6