该基于超图的高维聚类算法具有以下特点：1）能处理大数据集；
The algorithm could solve the problems of 1)large volume of data set; 2)data set of high dimension;
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。
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).
文介绍了一种聚类大型二元数据集合的快速算法，在该数据集合中数据点是高维的，并且大多数的坐标值为零。
This paper introduces a fast algorithm to cluster large binary data sets where data points have high dimensionality and most of their coordinates are zero.
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。
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.
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 data sets have features such as high-dimensional, sparseness and binary value in many clustering applications.
传统的中文文本聚类方法需要对高维向量进行处理，有对中文文本需要进行分词处理等困难。
Traditional method faces the difficulties that need to handle high dimension vector and Chinese word segment.
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.
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.
针对高属性维稀疏数据聚类问题，提出高属性维稀疏信息系统概念，给出一种新的基于稀疏特征差异度的动态抽象聚类方法。
The concepts of high attribute dimensional information system are firstly proposed, and a new dynamic clustering method on the basis of sparse feature difference degree is presented.
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.
针对高维大数据集聚类问题，提出了基于一维som最相似原型序列的聚类方法(MSPS - SOM)。
For the high dimensional and large data sets, a method called MSPS-SOM was proposed based on the most similar prototype sequence of one-dimensional SOM.
树型空间索引可以高效地组织并检索高维数据，因此使用树型空间索引是改善聚类性能的有力途径。
The structures and performances of all kinds of tree-like spatial indexes are analyzed in this paper.
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。
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.
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。
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.
实验结果表明，该算法能有效地对高维的方向性数据进行聚类。
The experiment results demonstrate its validity over directional higher-dimension data clustering.
实验结果表明，该算法能有效地对高维的方向性数据进行聚类。
The experiment results demonstrate its validity over directional higher-dimension data clustering.
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