High Dimensional Data Clustering的意思|示意

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。

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。

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。

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。