计算机工程与应用2011，Vol.47Issue(17)：134-136,3.DOI:10.3778/j.issn.1002-8331.2011.17.036
阈值优化的文本密度聚类算法
Text density clustering algorithm with optimized threshold values
摘要
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.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分类
信息技术与安全科学引用本文复制引用
马素琴,施化吉..阈值优化的文本密度聚类算法[J].计算机工程与应用,2011,47(17):134-136,3.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60841003) （the National Natural Science Foundation of China under Grant No.60841003）
国家火炬计划项目(No.2004EB33006). （No.2004EB33006）
