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.
该方法将在高属性维稀疏数据挖掘中起重要的作用。
The algorithm will have important application in high attribute dimensional data mining.
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.
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.
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.
该方法将在高属性维稀疏数据挖掘中起重要的作用。
The algorithm will have important application in high attribute dimensional dat…
该方法将在高属性维稀疏数据挖掘中起重要的作用。
The algorithm will have important application in high attribute dimensional dat…
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