TestData: Testing data for testing the performance of different...

In HighDimOut: Outlier Detection Algorithms for High-Dimensional Data

Description

The data are generated according to the example in the paper "Kriegel, Kroger, Schubert, and Arthur Zimek, 2009, Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data". The data has 60 rows and 3 variables. The first two variables are the x and y coordinates. The third variable indicates the type of data, i.e., "Pattern_1", "Pattern_2", or "Outlier". The first 25 observations belong to "Pattern_1". Another 25 observations represent "Pattern_2". The other 10 observations are "Outliers".

Usage

data(TestData)

Format

A data frame with 60 rows and 3 variables:

Examples

library(ggplot2)
data(TestData)
ggplot(data = TestData, aes(x = x, y = y, shape=Lab, color=Lab)) + geom_point()

Example output

HighDimOut documentation built on May 2, 2019, 12:16 p.m.