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改进的基于DBSCAN的空间聚类算法研究 被引量:1

Research on Improving Clustering Algorithm Based on DBSCAN
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摘要 针对DBSCAN算法I/O开销和内存消耗大的缺点,提出了基于层次合并的密度算法.该算法减少了DBSCAN算法中需要查询的点的数量,从而克服了DBSCAN算法I/O开销和内存消耗大的缺点.算法分析表明该算法对DBSCAN的改进是有效的. To deal with the limitation of DBSCAN which I/O spending and memory expand is very big, the clustering algorithm based density and hierarchical is presented in this thesis. It gets over the limitation of DBSCAN by reducing the number of points that needed to be found. The analysis proves the new algorithm is effective.
出处 《湖南理工学院学报(自然科学版)》 CAS 2008年第4期28-30,共3页 Journal of Hunan Institute of Science and Technology(Natural Sciences)
关键词 数据挖掘 空间数据挖掘 聚类分析 DBSCAN data mining spatial data mining Clustering analysis DBSCAN
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