Abstract: Document clustering had been employed in information filtering,web page classification and so on.K-means is one of the widely used clustering techniques because of its simplicity and high scalability.Owing to its random selection of initial centers,unstable results were often got when using traditional K-means and its variants.Here a technique of optimization initial centers of clustering was proposed.Combined with incremental iteration,it can produce clustering results with high purity,low entropy as well as good stableness.

Key words: optimize, document clustering, K-means

摘要： 文本聚类在信息过滤,网页分类中有着很好的应用。但它面临数据量大,特征维度高的难点。由于K平均算法易于实现,对数据依赖度底,在文本聚类中得到应用。然而,传统K平均以及它的变种会产生有较大波动的聚类结果。因此对K平均算法进行了改进,通过优化聚类初始中心的选择,得到一种适合对文本数据聚类分析的改进算法。大量实验显示,该算法可以生成质量较高而且聚类质量波动性较小的结果。

关键词: 优化, 文本聚类, K平均