本文构建了一种新的高维数据类模板。
This paper constructs one kind of new class template which deal with high dimension data.
高维数据建模和分析。
实现了基于数据降维的高维数据特征提取算法。
Realizes high, dimension data feature extraction algorithm by using debasing dimension of data.
计算主成分的目的是将高维数据投影到较低维空间。
The results of a PCA are usually discussed in terms of component scores and loadings (Shaw, 2003).
混合因子分析模型是一种非线性的分析高维数据的工具。
Mixtures of factor analyzers, is a nonlinear tool for high-dimension data.
目前，高维数据的快速检索问题已经受到越来越多的关注。
The query of High dimension data attracts more and more attention.
另一方面它能很好地处理高维数据和大数据集的数据表格。
Besides, it performs well when dealing with high dimensional data and has good scalability when the size of the data sets increases.
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 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。
This paper focuses mainly on investigating and studying clustering analysis problems of high directional dimensional data , which includes gene expression data and text data .
因此，对高维数据进行数据挖掘时，必须先对原始数据进行降维处理。
As a result, when doing data mining on high dimensional data, it is necessary to reduce the dimension of primal data at first.
天气也向提出运动使大高维数据集适合更远的分析，和相关方法公众。
It also addresses the movement to make large high-dimensional datasets public for further analysis, and the associated methods.
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.
实验证明了一系列算法的效率和有效性，尤其适合数据仓库中的高维数据集。
The effectiveness and efficiency of the algorithms have been shown by experimental results, especially for the high dimension data warehouses.
针对高维数据的相似性度量问题，提出了一种基于子空间的相似性度量方法。
Aiming at the similarity measurement of high dimensional data, the paper put forward a new method based on subspace.
高维数据的本征维数估计问题研究，在高维数据处理领域中有着重要的地位。
The intrinsic dimension estimation of high-dimensional data, is important in the field of high-dimensional data processing.
特征选择在处理具有较多不相关特征的高维数据上已被证明是一种有效的手段。
Feature selection has proven to be an effective means when dealing with large dimensionality with many irrelevant features.
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。
It is hard to cluster high-dimensional data using traditional clustering algorithm because of the sparsity of data.
支持向量机(SVM)高度的泛化能力使它特别适用于高维数据例如文档的分类。
The high generalization ability of Support Vector Machine (SVM) makes it especially suitable for the classification of high-dimension data such as term-document.
摘要：高维数据之间的相似性度量问题是高维空间数据挖掘中所面临的问题之一。
Absrtact: the problem of similarity measurement between high dimensional data is one of the problems high-dimensional data mining faces.
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。
In recent years, with the application of clustering, high dimensional data clustering is becoming more common, and more important.
针对中药方剂功效归纳问题，提出了一种基于人工神经网络新的高维数据归约方法。
A novel reduction method of high dimensions based on artificial neural network was proposed for the effect reduction of Chinese traditional medicine prescription.
在大规模多媒体数据库中进行基于内容的检索，高维数据索引结构的研究是重要问题。
The study of high dimensional data index method is the key problem of content based search in large scale multimedia databases.
数据维数的增加为人们提供了丰富的信息，也给高维数据的分析处理带来了极大的挑战。
Although the increasing of the dimensionality of the data provides us more rich information, it has brought us great challenge to deal with these data.
第2部分进行的是在投影寻踪思想下对高维数据主成分分析降维的理论分析和实践应用；
Part 2 is in the Projection Pursuit ideology high-dimensional data principal component analysis dimensionality reduction theoretical analysis and practical application;
在科学研究和日常生活中我们经常会遇到高维数据，它提供了极其丰富和详细的客观信息。
In scientific research and daily life, we always face with multi-dimension data which can indicate lots of detailed information.
应用主元分析方法将高维数据转换到低维数据空间，这使得过程监测可以在低维的空间内进行。
High dimension was changed into low dimension by using principal component analysis method, process detecting could be carried out in the low dimension space.
针对数据对象是高维数据的问题，将主成份分析方法应用到异常检测中解决数据集的降维问题。
Aiming at the problem that dataset is of high dimensions, principal components analysis is used in anomaly detection to reduce the dimensions of dataset.
针对数据对象是高维数据的问题，将主成份分析方法应用到异常检测中解决数据集的降维问题。
Aiming at the problem that dataset is of high dimensions, principal components analysis is used in anomaly detection to reduce the dimensions of dataset.
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