摘要： 高密度聚类作为数据挖掘中聚类算法的一种分析方法,它能找到样本比较密集的部分,并且概括出样本相对比较集中的类。分析了传统的聚类算法及局限性,讨论了一个基于高密度聚类算法的实现过程,使得算法可自动发现高维子空间,处理高维数据表格,得到较快的聚类速度和最佳的聚类效果。

关键词: 数据挖掘, 聚类, 高密度, DBSCAN

Abstract: As one analyzing method of clustering algorithm in data mining, DBSCAN finds relatively dense regions, which are clusters. DBSCAN is capable of eliminating outliers and discovering clusters of various shapes. The article analyses localization of the traditional clustering algorithm,discusses an implementation of DBSCAN. the algorithm digs out high dimension space ,deals with data form.The high accuracy and efficiency of DBSCAN clustering algorithm are shown in experiments.

中图分类号: 

• TP301.6