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
Datais NULL. - clusters
- An integer vector indicating the cluster partitioning
- Data
- The data matrix of the clustered observations. Required if
distanceis NULL. - method
- The metric used to determine the distance
matrix. Not used if
distanceis provided.
Details
The Dunn Index is the ratio of the smallest distance between observations not in the same cluster to the largest intra-cluster 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, 4:95-104. 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)
dunn(Dist, cluster)