dunn {clValid} | R Documentation |
Calculates the Dunn Index for a given clustering partition.
dunn(distance = NULL, clusters, Data = NULL, 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. |
method |
The metric used to determine the distance
matrix. Not used if distance is provided. |
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.
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, 4:95-104.
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) dunn(Dist, cluster)