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。
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。
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。
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。
Owing to the sparsity of high-dimensional data and the features of categorical data, it needs to develop special methods for high-dimensional categorical data.
现有的数据聚类方法仍存在着各种不足，聚类速度和结果的质量不能满足大型、高维数据库上的聚类需求。
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。
The intrinsic dimension estimation of high-dimensional data, is important in the field of high-dimensional data processing.
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。
High-dimensional data modeling and analysis.
高维数据建模和分析。
The sparsity and the problem of the curse of dimensionality of high-dimensional data, make the most of traditional clustering algorithms lose their action in high-dimensional space.
高维数据的稀疏性和“维灾”问题使得多数传统聚类算法失去作用，因此研究高维数据集的聚类算法己成为当前的一个热点。
Efficient indexing schemes for high-dimensional data are important for Content-Based Image Retrieval, with theoretical and applicable value as result.
有效的高维索引机制是基于内容的图像检索的关键技术，具有重要的理论意义和应用价值。
The complexity of feature selection for real-world data stream will increase because of high-dimensional data and concept drifting.
概念流动的出现及数据的高维性增加了数据流特征选择的复杂性。
Representation the high-dimensional data in a low-dimensional subspace is one of the fundamental problems in data analysis, pattern recognition, machine learning, and computer vision.
在低维空间描述高维数据是数据分析、模式识别、机器学习、计算机视觉等领域的基础问题之一。
The Locaally linear Embedding (LLE) algorithm is an effective technique for nonlinear dimensionality reduction of high-dimensional data.
局部线性嵌入(LLE)算法是有效的非线性降维方法，时间复杂度低并具有强的流形表达能力。
The Locaally linear Embedding (LLE) algorithm is an effective technique for nonlinear dimensionality reduction of high-dimensional data.
局部线性嵌入(LLE)算法是有效的非线性降维方法，时间复杂度低并具有强的流形表达能力。
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