文本聚类中基于密度聚类算法的研究与改进

文本聚类在很多领域都有广泛应用,而聚类算法作为文本聚类的核心直接决定了聚类的效果和效率。结合基于划分的聚类算法和基于密度的聚类算法的优点,提出了基于密度的聚类算法DBCKNN。算法利用了k近邻和离群度等概念,能够迅速确定数据集中每类的中心及其类半径,在保证聚类效果的基础上提高了聚类效率。

The research and improvement of density-based clustering algorithm in text clustering

Nowadays,the applications of text clustering are applied widely in many fields.the clustering algorithm as the core of text clustering directly determines the effectiveness and efficiency of clustering.In this paper,we combine the advantages between the partition-based clustering algorithm and the density-based clustering algorithm and propose a density-based clustering algorithm named DBCKNN.This algorithm by using the concepts of k-nearest neighbor and outlier degree can find the center and radius of each...