| connectivity {clValid} | R Documentation |
Calculates the connectivity validation measure for a given cluster partitioning.
connectivity(distance = NULL, clusters, Data = NULL, neighbSize = 10, method = "euclidean")
distance |
The distance matrix (as a matrix object) of the
clustered observations. Required if Data is NULL. |
clusters |
An integer vector indicating the cluster partitioning |
Data |
The data matrix of the clustered observations. Required if
distance is NULL. |
neighbSize |
The size of the neighborhood |
method |
The metric used to determine the distance
matrix. Not used if distance is provided. |
The connectivity indicates the degree of connectedness of the
clusters, as determined by the k-nearest 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 post-genomic data analysis. Bioinformatics 21(15): 3201-3212.
For a description of the function 'clValid' see clValid.
For a description of the class 'clValid' and all available methods see
clValidObj or clValid-class.
For additional help on the other validation measures see
dunn,
stability,
BHI, and
BSI.
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)