...间分析论文范文 关键词]高维稀疏数据；零子空间；子空间优化；高维数据预处理 [gap=598]Keywords: High dimensional sparse data, Zero-subspace, Subspace optimization, Preprocessing high dimension data ..
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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.
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