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。
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。
Part 2 is in the Projection Pursuit ideology high-dimensional data principal component analysis dimensionality reduction theoretical analysis and practical application;
第2部分进行的是在投影寻踪思想下对高维数据主成分分析降维的理论分析和实践应用；
Most of the real-world data, such as images and videos, are always represented by tensor and high-dimensionality form, which make tensor data dimensionality reduction the hot issue in recent research.
实际应用中的许多数据，如图像，视频，通常具有张量性和高维性特征，张量数据的维数约简便成为近期的研究热点。
The Locaally linear Embedding (LLE) algorithm is an effective technique for nonlinear dimensionality reduction of high-dimensional data.
局部线性嵌入(LLE)算法是有效的非线性降维方法，时间复杂度低并具有强的流形表达能力。
The Locaally linear Embedding (LLE) algorithm is an effective technique for nonlinear dimensionality reduction of high-dimensional data.
局部线性嵌入(LLE)算法是有效的非线性降维方法，时间复杂度低并具有强的流形表达能力。
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