But as was mentioned before, you should pick a different clustering criteria for geodetic data in any case.
但是，如前所述，在任何情况下，应该为大地数据选用一个不同的聚集标准。
Clustering improves availability by automatically synchronizing state data among all the nodes in a cluster.
此集群配置可自动在集群的所有节点之间同步状态数据，进而提高可用性。
Clustering data according to a certain property is a common and very useful technique to physically organize the data of a table.
根据某个属性聚集数据是一种常见的、也是非常有用的技术，这种技术可以物理地组织一个表的数据。
Clustering has its advantages when the data set is defined and a general pattern needs to be determined from the data.
当数据集已定义并且需要从此数据确定一个通用的模式时，群集的优势就会比较明显。
Clustering allows a user to make groups of data to determine patterns from the data.
群集让用户可以通过数据组来从数据确定模式。
The way to establish a certain data clustering property in DB2 is to reorganize the table according to an index.
在DB 2中建立数据聚集属性的方法是根据一个索引对表进行重组。
Clustering denotes a data mining technique that groups data records into clusters of pair-wise similar records by their properties.
集群是一种数据挖掘技术，这种技术根据数据记录的属性将相近的数据记录指定到集群中。
Multi DIMENSIONAL CLUSTERING (MDC) - organizing data in table (or range of a table) by multiple key values.
MULTIDIMENSIONALCLUSTERING (MDC)——根据多个键值组织表(或一个表中的范围)中的数据。
Similarly, if you are doing load balancing by clustering several computers, the session data is stored only on the computer that created it.
相似地，如果您正在通过群集几个计算机来实现负载平衡，那么会话数据只存储在创建它的计算机上。
However, for the average user, clustering can be the most useful data mining method you can use.
不过，对于一般的用户，群集有可能是最为有用的一种数据挖掘方法。
This can greatly improve the performance of queries that constrain and select data along one or multiple clustering dimensions.
这可极大改善从一个或多个集群维数限定并选择数据的那些查询的性能。
A requirement for clustering generally requires static data to be stored someplace besides in memory.
集群需求通常要求静态数据还存储在除内存之外的某个位置。
Data clustering can have an especially large impact on data warehouse query performance, because rows are often retrieved in large Numbers.
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。
Table 1 lists and describes some typical types of data-mining clustering.
表1列出并描述了一些典型类型的数据挖掘聚集。
The data set we'll use for our clustering example will focus on our fictional BMW dealership again.
我们为群集示例要使用的这个数据集同样也围绕着我们虚构的BMW经销店。
These recommendations address the database schema, the choice between XML and relational storage, definition of indexes, and physical data organization with partitioning and clustering options.
这些建议涉及了数据库模式、XML与关系存储之间的选择、索引的定义以及带有分区和集群选项的物理数据组织。
The clustering algorithm takes a data set and sorts them into groups, so you can make conclusions based on what trends you see within these groups.
群集算法是对一个数据集中的数据进行分组，以便您可以基于在这些组中看到的趋势得出结论。
A crucial step in the analysis process is to enable users to understand the results of the data clustering step.
在分析过程中，一个关键的步骤就是让用户理解数据集群步骤的结果。
This article discussed two data mining algorithms: the classification tree and clustering.
本文讨论了两种数据挖掘算法：分类树和群集。
Future articles will touch upon other methods of mining data, including clustering, Nearest Neighbor, and classification trees.
本系列后续的文章将会涉及挖掘数据的其他方法，包括群集、最近的邻居以及分类树。
The main purpose of allocating free space is to keep the data rows in the same physical sequence as the clustering index, thus reducing the need to frequently reorganize the data.
分配空余空间的主要目的是使数据行的物理顺序与群集索引一致，以减少频繁重组数据的需要。
Finally, the clustering of the data is guaranteed and cannot become un-clustered, as is normally the case in OLTP systems.
最后，数据的集群受到保障，数据不会又变成非集群状态，在OLTP系统中就常常会出现这种情况。
Data clustering offers a solution to this problem.
数据集群为这个问题提供了一个解决方案。
The team needs software for performing the clustering operations, data sharing, and so on.
团队需要软件来执行集群操作、数据共享等。
InfoSphere Warehouse USES a particularly powerful method for deviation detection that is based on data clustering.
InfoSphereWarehouse使用一种特别强大的方法来进行偏差检测，这种方法基于数据集群。
The result of the example shows that the new algorithm can efficiently solve data clustering analysis problems.
通过实例验证，表明该新算法能够有效地解决数据聚类分析问题。
In summary, the accuracy of statistics depends on the sampling rate, the data skew, and data clustering for data sampling.
总之，统计信息的准确性取决于抽样率、数据倾斜(data skew)以及用于数据抽样的数据群集。
Informix also remains ahead in areas such as clustering, data replication and grid-based computing.
Informix还在集群化、数据复制和基于网格的计算方面领先一步。
In view of the similarities between data clustering analysis and optimization questions, this paper deals with data clustering analysis by using simulation anneal algorithms.
本文针对数据聚类分析和最优化问题的相似点，用模拟退火算法进行聚类分析。
By contrasting the similarity and dissimilarity in data, clustering can find out the data's inner characteristic and distribution rule, so we can obtain the further understanding.
聚类通过比较数据的相似性和差异性，能发现数据的内在特征及分布规律，从而获得对数据更深刻的理解与认识。
应用推荐