数据聚簇对于数据仓库查询性能的影响尤其显著，因为常常在一个查询中获取许多行。
Data clustering can have an especially large impact on data warehouse query performance, because rows are often retrieved in large Numbers.
另外的解决方案，取决于具体的错误，可能是手工重建非聚簇索引，如果数据是静态的手工扔掉和重新载入表，诸如此类。
Additional solutions, depending on the errors, may be to manually rebuild non-clustered indexes, manually drop and reload a table if the data is static, and so on.
该算法将具有足够高密度的区域划分为簇，并可以在带有“噪声”的空间数据库中发现任意形状的聚类。
It can handle spatial data and spot any-shape clusters in a noised spatial database by dividing them into clusters with high enough density.
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