Exploring Multidimensional Data With the Flipped Empirical Distribution Function
Exploring Multidimensional Data With the Flipped Empirical Distribution Function
复制标题
使用翻转经验分布函数探索多维数据
DOI：
10.1080/10618600.1995.10474688
复制
发表时间：
1995
影响因子：
2.4
通讯作者：
M. Huh
中科院分区：
文献类型：
--
作者：
M. Huh
本文介绍了一种新的经验分布函数(EDF)，称为翻转经验分布函数(FEDF)，用于用图形表示单变量数据。因为绘图显示了各个点的位置，所以当我们需要操作特定的数据点时，如使用动态图形，它可能会很有用。本文介绍了利用FEDF浏览多维数据的几种方法。它们被称为平行FEDF、FEDF散点图矩阵和FEDF星图。当使用诸如选择、删除、链接、定位和识别一组数据点的动态图形方法来实现这些曲线图时，它们在探索多维数据方面的有用性变得更加突出。
Abstract This article introduces a new form of empirical distribution function (EDF) called the flipped empirical distribution function (FEDF), to represent univariate data graphically. Because the plot shows the location of individual points, it may be useful when we need to manipulate specific data points as with dynamic graphics. The article introduces several methods to explore multidimensional data using the FEDF. They are called a parallel FEDF, an FEDF scatterplot matrix, and an FEDF starplot. Usefulness of these plots in exploring multidimensional data becomes more prominent when they are implemented with the methods of dynamic graphics such as selecting, deleting, linking, locating, and identifying a group of data points.