Package: clustering.evaluation
Superclasses: clustering.evaluation.ClusterCriterion
DaviesBouldin criterion clustering evaluation object
clustering.evaluation.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. 
addK  Evaluate additional numbers of clusters 
compact  Compact clustering evaluation object 
plot  Plot clustering evaluation object criterion 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.