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Last data update: 2014.03.03

R: Partitioning Stage of the HDoutliers Algorithm

getHDmembers

R Documentation

Partitioning Stage of the HDoutliers Algorithm

Description

Implements the first stage of the HDoutliers Algorithm, in which the data is partitioned according to exemplars and their associated lists of members.

Usage

getHDmembers(data, maxrows = 10000, radius = NULL)

Arguments

`data`	A vector, matrix, or data frame consisting of numeric and/or categorical variables.
`maxrows`	If the number of observations is greater than `maxrows`, `HDoutliers` reduces the number used in nearest-neighbor computations to a set of exemplars. The default value is 10000.
`radius`	Threshold for determining membership in the exemplars's lists (used only when the number of observations is greater than `maxrows`). An observation is added to an exemplars' list if its distance to that exemplar is less than `radius`. The default value is .1/(log n)^(1/p), where n is the number of observations and p is the dimension of the data.

Details

If the number of observations exceeds maxrows, the data is partitioned into lists corresponding to exemplars and their members within radius of each exemplar, to reduce the number of nearest-neighbor computations required for outlier detection.
When there are fewer observations, the result is a list whose elements are the individual observations (each observation is an exemplar, with no other members).

Value

A list in which each component is a vector of observation indexes. The first index in each list is the index of the exemplar defining that list, and any remaining indexes are the associated members, within radius of the exemplar.

References

Wilkinson, L. (2016). Visualizing Outliers.

Examples


data(dots)
mem.W <- getHDmembers(dots$W)
out.W <- getHDoutliers(dots$W,mem.W)

data(ex2D)
mem.ex2D <- getHDmembers(ex2D)
out.ex2D <- getHDoutliers(ex2D,mem.ex2D)

## Not run: 
n <- 100000 # number of observations
set.seed(3)
x <- matrix(rnorm(2*n),n,2)
nout <- 10 # number of outliers
x[sample(1:n,size=nout),] <- 10*runif(2*nout,min=-1,max=1)

mem.x <- getHDmembers(x)
out.x <- getHDoutliers(x,mem.x)
## End(Not run)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(HDoutliers)
Loading required package: FNN
Loading required package: FactoMineR
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/HDoutliers/getHDmembers.Rd_%03d_medium.png", width=480, height=480)
> ### Name: getHDmembers
> ### Title: Partitioning Stage of the HDoutliers Algorithm
> ### Aliases: getHDmembers
> ### Keywords: cluster
> 
> ### ** Examples
> 
> 
> data(dots)
> mem.W <- getHDmembers(dots$W)
> out.W <- getHDoutliers(dots$W,mem.W)
> 
> data(ex2D)
> mem.ex2D <- getHDmembers(ex2D)
> out.ex2D <- getHDoutliers(ex2D,mem.ex2D)
> 
> ## Not run: 
> ##D n <- 100000 # number of observations
> ##D set.seed(3)
> ##D x <- matrix(rnorm(2*n),n,2)
> ##D nout <- 10 # number of outliers
> ##D x[sample(1:n,size=nout),] <- 10*runif(2*nout,min=-1,max=1)
> ##D 
> ##D mem.x <- getHDmembers(x)
> ##D out.x <- getHDoutliers(x,mem.x)
> ## End(Not run)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>