Description Usage Arguments Details Value Note Author(s) References See Also Examples
View source: R/clValidfunctions.R
Calculates the connectivity validation measure for a given cluster partitioning.
1 2  connectivity(distance = NULL, clusters, Data = NULL, neighbSize = 10,
method = "euclidean")

distance 
The distance matrix (as a matrix object) of the
clustered observations. Required if 
clusters 
An integer vector indicating the cluster partitioning 
Data 
The data matrix of the clustered observations. Required if

neighbSize 
The size of the neighborhood 
method 
The metric used to determine the distance
matrix. Not used if 
The connectivity indicates the degree of connectedness of the
clusters, as determined by the knearest neighbors. The
neighbSize
argument specifies the number of neighbors to use.
The connectivity has a value between 0 and infinity and should be minimized.
For details see the package vignette.
Returns the connectivity measure as a numeric value.
The main function for cluster validation is clValid
, and
users should call this function directly if possible.
Guy Brock, Vasyl Pihur, Susmita Datta, Somnath Datta
Handl, J., Knowles, K., and Kell, D. (2005). Computational cluster validation in postgenomic data analysis. Bioinformatics 21(15): 32013212.
For a description of the function 'clValid' see clValid
.
For a description of the class 'clValid' and all available methods see
clValidObj
or clValidclass
.
For additional help on the other validation measures see
dunn
,
stability
,
BHI
, and
BSI
.
1 2 3 4 5 6 7 8 9  data(mouse)
express < mouse[1:25,c("M1","M2","M3","NC1","NC2","NC3")]
rownames(express) < mouse$ID[1:25]
## hierarchical clustering
Dist < dist(express,method="euclidean")
clusterObj < hclust(Dist, method="average")
nc < 2 ## number of clusters
cluster < cutree(clusterObj,nc)
connectivity(Dist, cluster)

Loading required package: cluster
[1] 4.615873
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