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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
Existed data stream clustering algorithms can not deal with the data stream with high-dimensional heterogeneous attributes.
现有的数据流聚类算法无法处理高维混合属性的数据流。
Existed data stream clustering algorithms can not deal with the data stream with high-dimensional heterogeneous attributes.
现有的数据流聚类算法无法处理高维混合属性的数据流。
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