1) High-dimensional data
高维数据
1.
Classification research based on multiple fuzzy pattern recognition in high-dimensional data
高维数据多级模糊模式识别的分类研究
2.
Improved negative selection algorithm for network anomaly detection on high-dimensional data
高维数据环境下网络异常检测的改进否定选择算法
3.
In the paper the authors introduce three key methods of dimensionality reduction in high-dimensional dataset,such as MDS,Isomap.
本文介绍了MDS、Isomap等三种主要的高维数据降维方法,同时对这些降维方法的作用进行了探讨。
2) high dimensional data
高维数据
1.
Research of Method and Application on Dimensionality Reduction of High Dimensional Data Based on Multivariate Chart;
基于多元统计图的高维数据降维方法及应用研究
2.
Study of subspace clustering algorithm of high dimensional data based on variable weighting methods
基于可变加权的高维数据子空间聚类算法研究
3.
In this paper, a framework of a mapping-based clustering approach to deal with high dimensional data is proposed, and its performance analysis is also given.
本文提出了一个处理高维数据聚类的框架,并分析了该框架的性能。
3) high dimension data
高维数据
1.
The query of High dimension data attracts more and more attention.
目前,高维数据的快速检索问题已经受到越来越多的关注。
4) high dimensional data set
高维数据集
1.
The existing algorithms can compute k-dominant skylines for different k value(k≤d) in high dimensional data set,because of not sharing the result,lead to much repeated work.
现有的k支-配轮廓算法虽然可以对给定的高维数据集计算出不同k(k≤d)值对应的k-支配轮廓,但是,由于不能共享计算结果,会导致很多冗余操作。
5) high-dimensional categorical data
高维分类数据
1.
Owing to the sparsity of high-dimensional data and the features of categorical data,it needs to develop special methods for high-dimensional categorical data.
鉴于高维数据的稀疏性和分类数据特点,探讨了专门针对高维分类数据的聚类方法。
6) high-dimensional cube
高维数据立方体
1.
But in a high-dimensional cube,it might not be practical to build all these cuboids.
为解决高维聚集海量数据的存储与查询问题,通过分段共享数据立方体技术,将高维数据立方体划分成若干个低维数据立方体,并利用并行处理技术来创建这些分割的分段共享数据立方体及其聚集数据立方体,以实现高维数据立方体的并行创建和增量更新维护。
补充资料：数据通信网(见数据通信)
数据通信网(见数据通信)
data communication network
shu)u tongxinwang数据通信网(datac。mmunicati。nne饰ork)见数据通信。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条