connectivity {clv} | R Documentation |
Function evaluates connectivity index.
connectivity(data,clust,neighbour.num, dist="euclidean") connectivity.diss.mx(diss.mx,clust,neighbour.num)
data |
numeric matrix or data.frame where columns correspond to variables and rows to
observations
|
diss.mx |
square, symetric numeric matrix or data.frame , representation of
dissimilarity matrix where infomartion about distances between objects is stored.
|
clust |
integer vector with information about cluster id the object is assigned to.
If vector is not integer type, it will be coerced with warning.
|
neighbour.num |
value which tells how many nearest neighbors for every object should be checked. |
dist |
chosen metric: "euclidean" (default value), "manhattan", "correlation"
(variable enable only in connectivity function). |
For given data and its partitioning connectivity index is computed.
For choosen pattern neighbour.num
nearest neighbours are found and sorted from closest
to most further. Alghorithm checks if those neighbours are
assigned to the same cluster. At the beggining connectivity value is equal 0 and increase
with value:
1/i | when i-th nearest neighbour is not assigned to the same cluster, |
0 | otherwise. |
Procedure is repeated for all patterns which comming from our data set. All values received for particular pattern are added and creates main connectivity index.
connectivity
returns a connectivity value.
Lukasz Nieweglowski
J. Handl, J. Knowles and D. B. Kell Sumplementary material to computational cluster validation in post-genomic data analysis, http://dbkgroup.org/handl/clustervalidation/supplementary.pdf
# load and prepare data library(clv) data(iris) iris.data <- iris[,1:4] # cluster data pam.mod <- pam(iris.data,5) # create five clusters v.pred <- as.integer(pam.mod$clustering) # get cluster ids associated to gived data objects # compute connectivity index using data and its clusterization conn1 <- connectivity(iris.data, v.pred, 10) conn2 <- connectivity(iris.data, v.pred, 10, dist="manhattan") conn3 <- connectivity(iris.data, v.pred, 10, dist="correlation") # the same using dissimilarity matrix iris.diss.mx <- as.matrix(daisy(iris.data)) conn4 <- connectivity.diss.mx(iris.diss.mx, v.pred, 10)