something that can be coerced into a numerical
matrix. Euclidean dataset.
clustering
vector of integers with length =nrow(x);
indicating the cluster for each observation.
r
integer. Number of cluster border representatives.
s
numerical vector of shrinking factors (between 0 and 1).
clusterstdev
logical. If TRUE, the neighborhood radius
for intra-cluster density is the within-cluster estimated squared
distance from the mean of the cluster; otherwise it is the average of
these over all clusters.
trace
logical. If TRUE, results are printed for the
steps to compute the index.
Value
List with components (see Halkidi and Vazirgiannis (2008), Halkidi et
al. (2015) for details)
cdbw
value of CDbw index (the higher the better).
cohesion
cohesion.
compactness
compactness.
sep
separation.
References
Halkidi, M. and Vazirgiannis, M. (2008) A density-based cluster
validity approach using multi-representatives. Pattern
Recognition Letters 29, 773-786.
Halkidi, M., Vazirgiannis, M. and Hennig, C. (2015) Method-independent
indices for cluster validation. In C. Hennig, M. Meila, F. Murtagh,
R. Rocci (eds.) Handbook of Cluster Analysis, CRC
Press/Taylor & Francis, Boca Raton.