With the growth of data in volume and dimensionality, it has become a very challenging problem to build a high-efficient classifier for large databases.

随着数据集的数据量和维数的增加，建立高效的、适用于大型数据集的分类法已成为数据挖掘的一个挑战性问题。

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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。

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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。

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