dunn
Dunn Index
Calculates the Dunn Index for a given clustering partition.
 Keywords
 cluster
Usage
dunn(distance = NULL, clusters, Data = NULL, 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.  method
 The metric used to determine the distance
matrix. Not used if
distance
is provided.
Details
The Dunn Index is the ratio of the smallest distance between observations not in the same cluster to the largest intracluster distance. The Dunn Index has a value between zero and infinity, and should be maximized. For details see the package vignette.
Value

Returns the Dunn Index as a numeric value.
Note
The main function for cluster validation is clValid
, and
users should call this function directly if possible.
References
Dunn, J.C. (1974). Well separated clusters and fuzzy partitions. Journal on Cybernetics, 4:95104. Handl, J., Knowles, K., and Kell, D. (2005). Computational cluster validation in postgenomic data analysis. Bioinformatics 21(15): 32013212.
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 clValidclass
.
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)
dunn(Dist, cluster)