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。
Data clustering is an important problem in data mining.
数据聚类是数据挖掘中的一个重要课题。
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。
The experiment results demonstrate its validity over directional higher-dimension data clustering.
实验结果表明，该算法能有效地对高维的方向性数据进行聚类。
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。
The universality of these data makes researches on high dimensional data clustering more and more important.
由于高维数据存在的普遍性，高维数据的聚类分析具有非常重要的意义。
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。
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。
Meanwhile, the research of the stream data clustering algorithm would be useful references to the similar researches.
同时，本文对流数据聚类算法的研究，对于促进同类问题的研究具有一定的理论价值和借鉴意义。
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。
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。
This algorithm can be used in data clustering and face detection. Its effectiveness has been proven by the experiment results.
这个算法可以用于数据聚类和人脸识别方面，实验结果也证明了该算法的效果。
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。
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。
Self Organizing Map is a method of artificial neural network, which implements pattern recognition and data clustering simultaneously.
自组织特征映射是一种人工神经网络方法，可以同时实现模式识别和数据分类。
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。
Based on the traditional fuzzy C-means clustering algorithm, a new fuzzy C-means clustering algorithm for interval data clustering is proposed.
在传统模糊c -均值聚类算法的基础上，提出了一种新型区间值数据模糊聚类算法。
In data Clustering Analysis technology, the data has been divided into natural colony, and each colony characteristic describes one data Mining Method.
聚类分析技术就是将数据区分为自然的群体，并给出每个群体特征描述的一种数据挖掘方法。
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；
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。
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。
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。
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 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。
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。
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。
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