connectivity {clValid}

Connectivity Measure

Description

Calculates the connectivity validation measure for a given cluster partitioning.

Usage

connectivity(distance = NULL, clusters, Data = NULL, neighbSize = 10,
             method = "euclidean")

Arguments

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.

Details

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.

Value

Returns the connectivity measure as a numeric value.

Note

The main function for cluster validation is clValid, and users should call this function directly if possible.

Author(s)

Guy Brock, Vasyl Pihur, Susmita Datta, Somnath Datta

References

Handl, J., Knowles, K., and Kell, D. (2005). Computational cluster validation in post-genomic data analysis. Bioinformatics 21(15): 3201-3212.

See Also

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.

Examples

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)