Using multi-dimensional clustering for query performance.
使用多维聚簇提高查询性能。
Multi DIMENSIONAL CLUSTERING (MDC) - organizing data in table (or range of a table) by multiple key values.
MULTIDIMENSIONAL CLUSTERING (MDC)——根据多个键值组织表(或一个表中的范围)中的数据。
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。
To overcome the shortcomings of the GCOD, a high-dimensional clustering algorithm for data mining, the paper proposes an intersected grid clustering algorithm based on density estimation (IGCOD).
针对高维聚类算法——相交网格划分算法GCOD存在的缺陷，提出了基于密度度量的相交网格划分聚类算法IGCOD。
In many implementations of clustering, items in the collection are represented as vectors in an n-dimensional space.
在许多集群实现中，集合中的项目都是作为矢量表示在n维度空间中的。
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。
The algorithm of two-dimensional Kohonen network is improved from serval aspects such as neighborhood function, learning rate, etc. It is applied into tobacco clustering.
本章重点从邻域函数、学习率调整等方面研究了二维网络的改进算法，并将之应用于烟叶动态分类问题。
This paper introduces a newly generalized and dynamic structure for the similarity retrieval of high dimensional feature vectors called the recursive clustering index tree.
文章提出了一种新的适用于高维特征矢量相似检索动态聚类索引树结构。
This paper focuses mainly on investigating and studying clustering analysis problems of high directional dimensional data , which includes gene expression data and text data .
本文针对高维数据的方向性及其聚类分析中出现的问题进行了研究。
Facing the massive volume and high dimensional data how to build effective and scalable clustering algorithm for data mining is one of research directions of data mining.
面对大规模的、高维的数据，如何建立有效、可扩展的的聚类数据挖掘算法是数据挖掘领域的一个研究热点。
The concepts of high attribute dimensional information system are firstly proposed, and a new dynamic clustering method on the basis of sparse feature difference degree is presented.
针对高属性维稀疏数据聚类问题，提出高属性维稀疏信息系统概念，给出一种新的基于稀疏特征差异度的动态抽象聚类方法。
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。
It is hard to cluster high-dimensional data using traditional clustering algorithm because of the sparsity of data.
在高维空间中，由于数据的稀疏性，传统的聚类方法难以有效地聚类高维数据。
This model can transform units bidding curve of power producer in market into a one-dimensional feature vector, so it can implement classification of units bidding using classical clustering method.
利用平均电价差值积分模型将电力市场中发电商的机组报价曲线转换为一维特征向量，从而采用传统聚类方法对机组报价曲线实现分类。
The data sets have features such as high-dimensional, sparseness and binary value in many clustering applications.
在许多聚类应用中，数据对象是具有高维、稀疏、二元的特征。
In this paper, a framework of a mapping-based clustering approach to deal with high dimensional data is proposed, and its performance analysis is also given.
本文提出了一个处理高维数据聚类的框架，并分析了该框架的性能。
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。
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。
The foundations were established for the exploration of serial clustering methods based on one-dimensional SOM.
为开发基于一维som的系列聚类分析法奠定了基础。
With the expansion of the application field of clustering analysis, more and more high-dimensional and mixed-type data need to be processed.
随着聚类分析的应用领域日益扩展，越来越多高维的、混合类型属性数据需要处理。
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。
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 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。
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