Finding representatives for cluster border
Finds representative objects for the border of a cluster and the
within-cluster variance as defined in the framework of the cdbw
cluster validation index (and meant to be used in that context).
findrep(x,xcen,clustering,cluster,r,p=ncol(x),n=nrow(x),
nc=sum(clustering==cluster))x |
matrix. Euclidean dataset. |
xcen |
mean vector of cluster. |
clustering |
vector of integers with length |
cluster |
integer. Number of cluster to be treated. |
r |
integer. Number of representatives. |
p |
integer. Number of dimensions. |
n |
integer. Number of observations. |
nc |
integer. Number of observations in |
List with components
repc |
vector of index of representatives (out of all observations). |
repx |
vector of index of representatives (out of only the
observations in |
maxr |
number of representatives (this can be smaller than
|
wvar |
estimated average within-cluster squared distance to mean. |
Halkidi, M. and Vazirgiannis, M. (2008) A density-based cluster validity approach using multi-representatives. Pattern Recognition Letters 29, 773-786.
Halkidi, M., Vazirgiannis, M. and Hennig, C. (2015) Method-independent
indices for cluster validation. In C. Hennig, M. Meila, F. Murtagh,
R. Rocci (eds.) Handbook of Cluster Analysis, CRC
Press/Taylor & Francis, Boca Raton.
options(digits=3) iriss <- as.matrix(iris[c(1:5,51:55,101:105),-5]) irisc <- as.numeric(iris[c(1:5,51:55,101:105),5]) findrep(iriss,colMeans(iriss),irisc,cluster=1,r=2)
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