• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2013 IEEE International Conference on High Performance Computing and Communications amp; 2013 IEEE International Conference on Embedded and Ubiquitous Computing >Measuring and Discovering Correlations in Large Data Sets
【24h】

Measuring and Discovering Correlations in Large Data Sets

机译：测量和发现大数据集中的相关性

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, a class of statistics named ART (the alternant recursive topology statistics) is proposed to measure the properties of correlation between two variables. A wide range of bi-variable correlations both linear and nonlinear can be evaluated by ART efficiently and equitably even if nothing is known about the specific types of those relationships. ART compensates the disadvantages of Reshef's model in which no polynomial time precise algorithm exists and the "local random" phenomenon can not be identified. As a class of nonparametric exploration statistics, ART is applied for analyzing a dataset of 10 American classical indexes, as a result, lots of bi-variable correlations are discovered.
机译：在本文中，提出了一种称为ART（交替递归拓扑统计）的统计量，以测量两个变量之间的相关性。即使对于这些关系的具体类型一无所知，ART可以有效，公平地评估线性和非线性的各种双变量相关性。 ART弥补了Reshef模型的缺点，在Reshef模型中，不存在多项式时间精确算法，无法识别“局部随机”现象。作为一类非参数勘探统计数据，ART被用于分析10个美国古典指标的数据集，结果发现了许多双变量相关性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号