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。
High dimensionality is the main difficulty of similarity search over time-series data.
数据的高维度是造成时序数据相似性搜索困难的主要原因。
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.
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。
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