Package: clustering.evaluation
Superclasses: clustering.evaluation.ClusterCriterion
DaviesBouldin criterion clustering evaluation object
DaviesBouldinEvaluation
is an object consisting of sample data,
clustering data, and DaviesBouldin criterion values used to evaluate the optimal number
of clusters. Create a DaviesBouldin criterion clustering evaluation object using
evalclusters
.
creates a DaviesBouldin criterion clustering evaluation object.eva
= evalclusters(x
,clust
,'DaviesBouldin')
creates a DaviesBouldin criterion clustering evaluation object using additional options
specified by one or more namevalue pair arguments.eva
= evalclusters(x
,clust
,'DaviesBouldin',Name,Value
)

Clustering algorithm used to cluster the input data, stored
as a valid clustering algorithm name or function handle. If the clustering
solutions are provided in the input, 

Name of the criterion used for clustering evaluation, stored as a valid criterion name. 

Criterion values corresponding to each proposed number of clusters
in 

List of the number of proposed clusters for which to compute criterion values, stored as a vector of positive integer values. 

Logical flag for excluded data, stored as a column vector of
logical values. If 

Number of observations in the data matrix 

Optimal number of clusters, stored as a positive integer value. 

Optimal clustering solution corresponding to 

Data used for clustering, stored as a matrix of numerical values. 
[1] Davies, D. L., and D. W. Bouldin. “A Cluster Separation Measure.” IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. PAMI1, No. 2, 1979, pp. 224–227.
CalinskiHarabaszEvaluation
 GapEvaluation
 SilhouetteEvaluation
 evalclusters