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。
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。
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。
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。
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。
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。
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。
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。
应用推荐