Using multi-dimensional clustering for query performance.
使用多维聚簇提高查询性能。
As in previous releases, administrators use the ORGANIZE BY dimension (...) clause of the CREATE TABLE statement to specify multi-dimensional clustering.
与以前的版本一样，管理员使用CREATEtable语句的ORGANIZEBYDIMENSION(…)子句指定多维聚簇。
For best performance and minimal space consumption, we recommend using database partitioning, range partitioning, compression, and multi-dimensional clustering.
为了获得更好的性能和最小化空间消耗，我们建议使用数据库分区、范围分区、压缩和多维集群。
To support such a query, an administrator can use multi-dimensional clustering to instruct DB2 to physically organize rows in the SALES table by these dimensions.
为了支持这样的查询，管理员可以通过使用多维聚簇让DB 2按这些维组织sales表中的行。
With DB2 9.7, tables containing XML columns can participate in multi-dimensional clustering, provided that the clustering dimensions are defined by relational columns.
在DB 2 9.7中，包含xml列的表可以应用多维聚簇，但是聚簇维必须由关系列定义。
Multi DIMENSIONAL CLUSTERING (MDC) - organizing data in table (or range of a table) by multiple key values.
MULTI DIMENSIONAL CLUSTERING (MDC)——根据多个键值组织表(或一个表中的范围)中的数据。
A dynamic clustering algorithm was proposed based on consistent matrix of dependent function for time series multi-dimensional data.
根据时序立体数据的特点，提出了基于关联函数一致性矩阵的动态聚类算法。
Proposed dynamic clustering algorithm has strong robustness in clustering of time series multi-dimensional data.
本文算法对于时序数据的聚类具有较强的鲁棒性。
This algorithm combined the fuzzy clustering of multi-dimensional data with CTWC. Furthermore, it introduced the norm-based method to improve and prove reasonable.
该算法把多维数据的模糊聚类方法与CTWC相结合，并引入基于范数的方法进一步对该方法加以改进和论证。
And based on the experimental results of multi-dimensional data clustering, anomaly detection matrix is determined through identifying the training sample set and the machine self-learning.
然后根据对多维数据聚类的实验分析结果，通过对样本集的训练进行标识和机器自学习过程来判别异常检测矩阵。
And based on the experimental results of multi-dimensional data clustering, anomaly detection matrix is determined through identifying the training sample set and the machine self-learning.
然后根据对多维数据聚类的实验分析结果，通过对样本集的训练进行标识和机器自学习过程来判别异常检测矩阵。
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