cgrestandard | R Documentation |
Standardises cluster validity statistics as produced by
clustatsum
relative to results that were achieved by
random clusterings on the same data by
randomclustersim
. The aim is to make differences between
values comparable between indexes, see Hennig (2019), Akhanli and
Hennig (2020).
This is mainly for use within clusterbenchstats
.
cgrestandard(clusum,clusim,G,percentage=FALSE, useallmethods=FALSE, useallg=FALSE, othernc=list())
clusum |
object of class "valstat", see |
clusim |
list; output object of |
G |
vector of integers. Numbers of clusters to consider. |
percentage |
logical. If |
useallmethods |
logical. If |
useallg |
logical. If |
othernc |
list of integer vectors of length 2. This allows the
incorporation of methods that bring forth other numbers of clusters
than those in |
cgrestandard
will add a statistic named dmode
to the
input set of validation statistics, which is defined as
0.75*dindex+0.25*highdgap
, aggregating these two closely
related statistics, see clustatsum
.
List of class "valstat"
, see
valstat.object
, with standardised results as
explained above.
Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/
Hennig, C. (2019) Cluster validation by measurement of clustering characteristics relevant to the user. In C. H. Skiadas (ed.) Data Analysis and Applications 1: Clustering and Regression, Modeling-estimating, Forecasting and Data Mining, Volume 2, Wiley, New York 1-24, https://arxiv.org/abs/1703.09282
Akhanli, S. and Hennig, C. (2020) Calibrating and aggregating cluster validity indexes for context-adapted comparison of clusterings. Statistics and Computing, 30, 1523-1544, https://link.springer.com/article/10.1007/s11222-020-09958-2, https://arxiv.org/abs/2002.01822
valstat.object
, clusterbenchstats
, stupidkcentroids
, stupidknn
, \codestupidkfn, stupidkaven
, codeclustatsum
set.seed(20000) options(digits=3) face <- rFace(10,dMoNo=2,dNoEy=0,p=2) dif <- dist(face) clusum <- list() clusum[[2]] <- list() cl12 <- kmeansCBI(face,2) cl13 <- kmeansCBI(face,3) cl22 <- claraCBI(face,2) cl23 <- claraCBI(face,2) ccl12 <- clustatsum(dif,cl12$partition) ccl13 <- clustatsum(dif,cl13$partition) ccl22 <- clustatsum(dif,cl22$partition) ccl23 <- clustatsum(dif,cl23$partition) clusum[[1]] <- list() clusum[[1]][[2]] <- ccl12 clusum[[1]][[3]] <- ccl13 clusum[[2]][[2]] <- ccl22 clusum[[2]][[3]] <- ccl23 clusum$maxG <- 3 clusum$minG <- 2 clusum$method <- c("kmeansCBI","claraCBI") clusum$name <- c("kmeansCBI","claraCBI") clusim <- randomclustersim(dist(face),G=2:3,nnruns=1,kmruns=1, fnruns=1,avenruns=1,monitor=FALSE) cgr <- cgrestandard(clusum,clusim,2:3) cgr2 <- cgrestandard(clusum,clusim,2:3,useallg=TRUE) cgr3 <- cgrestandard(clusum,clusim,2:3,percentage=TRUE) print(str(cgr)) print(str(cgr2)) print(cgr3[[1]][[2]])
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