基于相对密度的多耦合文本聚类算法*

针对现有的空间向量模型在进行文本表示时忽略词条的位置和词条间关系的问题，提出了一种基于相对密度的多耦合文本聚类算法。该算法在基于相对密度的聚类方法基础上，根据相对密度越小本文相似性越小这一事实，将相对密度转化为文本相似度，融入了传统DBSCAN密度算法，并对核心对象的选取进行了优化，。实验结果表明，与改进的K-means文本聚类和改进的DBSCAN文本聚类算法相比，本算法在文本聚类中更高效、聚类质量更优。

A Multi-Coupled Text Clustering Algorithm Based On Relative Density

Aim at the problem that existing space vector models ignore entry location and the relations between entries, a multi-coupled text clustering algorithm based on relative density is proposed in the paper. On the basis of clustering algorithms based on relative density and according to the reality that the smaller the relative density is, the smaller the text similarity is, the algorithm converts relative density to text similarity, integrates the traditional DBSCAN density algorithm, and optimize the selection of core objects. Experimental results show that, Comparing with the improved k-means and the improved DBSCAN text clustering algorithms, the proposed algorithm performs more efficiently and has a better clustering quality in text clustering.