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。
Data mining commonly involves a few standard tasks that include clustering, classification, regression, and associated rule learning.
数据挖掘通常涉及到一些标准的任务，包括聚集、分类、回归分析和关联性规则学习。
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。
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。
The effects of clustering the rows by their spatial properties rely on accessing just a subset of the data during query time.
按照空间属性聚集这些行的效果取决于在查询时对一个数据子集的访问。
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。
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。
This paper proposes a solving method of grid granularity in spatial data clustering.
提出一种空间数据聚类中的网格粒度求解方法。
We design and implement the artificial immune network algorithm, and successfully apply this algorithm in solving a pattern recognition problem and a data clustering problem.
在此基础上，设计和实现了人工免疫网络算法，并应用该算法成功解决了一个模式识别和数据聚类问题。
In recent years, with the application of clustering, high dimensional data clustering is becoming more common, and more important.
近年来随着聚类应用领域的扩展和深入，高维数据聚类越来越普遍，也越来越重要。
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。
Combined with semantic similarity of text data, this paper gives a method of text data clustering based on semantic density.
结合文本数据的语义相似度，给出一种基于语义密度文本数据聚类的方法。
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。
Data mining is the core topic of this paper. Basically, it includes associate rule founding, data clustering and data assorting.
数据挖掘是本课题的研究核心，主要包括关联规则发现、数据聚类和数据分类。
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。
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。
Seven kinds of spatial data clustering approaches are studied. And the technique to solve the problem of Constraint-based Spatial Cluster Analysis is explored.
系统研究了七种典型的空间数据聚类方法，积极探索基于约束条件的空间聚类问题的解决方案；
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。
One defining benefit of clustering over classification is that every attribute in the data set will be used to analyze the data.
与分类相比，群集的一个好处是数据集内的每个属性都被用来分析该数据。
In the next version of the SAP BW product, the Fact tables will use a multidimensional clustering (MDC) table to store data.
在下一个版本的SAPBW产品中，fact表将使用多维集群(multidimensional clustering,MDC)表来存储数据。
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。
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。
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。
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 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。
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。
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。
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。
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