The consistency of BICC in high dimensional data model selection is also shown.
本文证明了这种信息准则在模型选择方面具有一致性。
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。
Aiming at the similarity measurement of high dimensional data, the paper put forward a new method based on subspace.
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。
As a result, when doing data mining on high dimensional data, it is necessary to reduce the dimension of primal data at first.
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。
The study of high dimensional data index method is the key problem of content based search in large scale multimedia databases.
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。
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。
The splitting strategy for high dimensional data set is important for the performance of the indexing of high-dimensional database.
数据集的划分策略是影响高维数据库索引性能的一个关键因素。
Besides, it performs well when dealing with high dimensional data and has good scalability when the size of the data sets increases.
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。
Absrtact: the problem of similarity measurement between high dimensional data is one of the problems high-dimensional data mining faces.
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。
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。
Facing the massive volume and high dimensional data, how to build effective and scalable algorithm for data mining is one of research directions of data mining.
面对大规模、高维的数据，如何建立有效的，可扩展的分类数据挖掘算法是数据挖掘研究的重要方向之一。
By designing projection index it projects high dimensional data set to low dimensional space to reveal the internal structures and characters of high dimensional dataset.
投影寻踪方法是根据特定的应用意义设计相应的投影指标，把高维数据集投影到低维数据空间后进行分析，揭示高维数据集内部的结构和特征。
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。
Data mining is about digging up interesting information from this high-dimensional data.
数据挖掘便是要从高维数据中挖出那些令人感兴趣的信息来。
Venkatasubramanian and colleagues performed a series of tests of their new method with "synthetic data" - data points in a "high-dimensional space."
文卡和同事们用“模拟数据”(“高维空间”的数据点)完成了这种新方法的一系列测试。
These strings of hundreds of attributes are called high-dimensional data because each attribute is called one dimension.
文卡说道，“这些包含数百项属性的字符串就称为高维（high-dimensional）数据，而每一项属性就是一维。
Nowadays the applications of spatial data similarity search are widely needed, and high dimensional spatial data index becomes a key technology of similarity search.
目前空间数据相似性查询有着广泛的应用需求，解决相似性查询问题的一项关键技术就是高维空间数据索引。
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。
The algorithm will have important application in high attribute dimensional data mining.
该方法将在高属性维稀疏数据挖掘中起重要的作用。
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。
High-dimensional data modeling and analysis.
高维数据建模和分析。
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。
These plentiful data and their high dimensional character make the traditional data analysis method be outshone.
这些海量数据及其高维特征使得传统的数据分析手段相形见绌。
However, conventional SOMs handle only numerical data, categorical data has to be converted to Boolean data resulting in unable to disclosure the similarity among the high-dimensional data.
然而，传统自组映射图只能处理数值型资料，种类型资料必须透过编码转换成一群二元数值型态资料，因而无法反映种类型资料值之间的相似程度。
This method develop one-dimensional MAW method into high-dimensional DLB method by introducing HSFC, block data structure and even more measurement information.
它把仅适用于一维的多层均权法扩展到二维和三维，并引入更多的实测信息和块数据结构。
The SVM (Support vector Machine) classifies the data by mapping the vector from low-dimensional space to high-dimensional space using kernel function.
而SVM(支持向量机)引进核函数隐含的映射把低维特征空间中的样本数据映射到高维特征空间来实现分类。
Venkatasubramanian and colleagues performed a series of tests of their new method with "synthetic data" - data points in a "high-dimensional space. "
文卡和同事们用“模拟数据”（“高维空间”的数据点）完成了这种新方法的一系列测试。
Venkatasubramanian and colleagues performed a series of tests of their new method with "synthetic data" - data points in a "high-dimensional space. "
文卡和同事们用“模拟数据”（“高维空间”的数据点）完成了这种新方法的一系列测试。
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