Calculates the Dunn Index for a given clustering partition.
1 
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

method 
The metric used to determine the distance
matrix. Not used if 
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.
Returns the Dunn Index 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
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.
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)
dunn(Dist, cluster)

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
Please suggest features or report bugs with the GitHub issue tracker.
All documentation is copyright its authors; we didn't write any of that.