关键词: 文本挖掘, 文本聚类, 一个基于高密度连接区域的密度聚类方法, 一种阈值优化的文本密度聚类算法, 分位数

Abstract: A text density clustering algorithm with the optimized threshold values is proposed to solve the problem of reduced clustering performance of the DBSCAN algorithm because of global threshold values.The proposed algorithm sorts objects with k-neighbor distance，and discerns arrays with different densities by quantile，and finds the corresponding optimization，then carries out clustering of objects using density clustering algorithm based on optimized threshold values.The advanced clustering algorithm has overcome the problem of reduced clustering performance caused by threshold values selection，and has improved clustering accuracy and efficiency.This paper stores clusters with tree structure，and has made clusters more legible.The experimental results show the effectiveness of this algorithm.

Key words: text mining, text clustering, Density-Based Spatial Clustering of Applications with Noise（DBSCAN） algorithm, Text Density Clustering Algorithm with Optimized Threshold Values（TDCAOTV） algorithm, quantile