Ps22Pdf 关键词 : 大规模数据库 ; 聚类 ; 数据交叠分区 ; DBSCAN 算法 ; 并行计算 [gap=781]Key words: Massive Database; Clustering; Data-overlap-partition; DBSCAN Algorithm; Parallel Computing ..
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该算法显著减少了已有算法中产生频繁项集及扫描大规模数据库的操作，性能改善明显。
The performance of this algorithm is improved noticeably by reducing the operation of producing frequent item sets and scanning large scale databases.
超级计算机通常用于需要执行大量计算、处理大规模数据库或者二者兼具的科学上和工程上的应用程序。
A supercomputer is typically used for scientific and engineering applications that perform a large amount of computation, handle massive databases, or both.
迄今为止人们提出了许多用于大规模数据库的聚类算法。基于密度的聚类算法DBSCAN就是一个典型代表。
In this paper, a fast density based clustering algorithm is developed, which considerably speeds up the original DBSCAN algorithm.
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