index.DB {clusterSim} | R Documentation |
Calculates Davies-Bouldin's cluster separation measure
index.DB(x, cl, centrotypes="centroids", p=2, q=2)
x |
data |
cl |
vector of integers indicating the cluster to which each object is allocated |
centrotypes |
"centroids" or "medoids" |
p |
the power of the Minkowski distance between centroids or medoids of clusters: p=1 - Manhattan distance; p=2 - Euclidean distance |
q |
the power of dispersion measure of a cluster: q=1 - the average distance of objects in the r-th cluster to the centroid or medoid of the r-th cluster; q=2 - the standard deviation of the distance of objects in the r-th cluster to the centroid or medoid of the r-th cluster |
See file $R_HOME\library\clusterSim\pdf\indexDB_details.pdf for further details
DB |
Davies-Bouldin's index |
r |
vector of maximal R values for each cluster |
R |
R matrix $(S_r+S_s)/d_{rs}$ |
d |
matrix of distances between centroids or medoids of clusters |
S |
vector of dispersion measures for each cluster |
centers |
coordinates of centroids or medoids for all clusters |
Marek Walesiak Marek.Walesiak@ae.jgora.pl, Andrzej Dudek Andrzej.Dudek@ae.jgora.pl
Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland http://www.ae.jgora.pl/keii
Davies, D.L., Bouldin, D.W. (1979), A cluster separation measure, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. 2, 224-227.
index.G1
, index.G2
, index.G3
,
index.S
, index.H
, index.Gap
, index.KL
#Example 1 library(clusterSim) data(data_ratio) cl1 <- pam(data_ratio, 4) print(index.DB(data_ratio, cl1$clustering, centrotypes="medoids")) #Example 2 library(clusterSim) data(data_ratio) cl2 <- pam(data_ratio, 5) print(index.DB(data_ratio, cl2$clustering, centrotypes="centroids"))