nomclust (version 2.1.6)

evalclust: Evaluation of Hierarchical Clustering for Nominal Data

Description

The evalclust function calculates a set of evaluation criteria, see (Sulc et al., 2018) and provides the optimal number of clusters based on these criteria. It is primarily focused on the evaluation of hierarchical clustering results obtained by similarity measures different from the ones that occur in the nomclust package. Thus, it can serve for comparison of various similarity measures for categorical data.

Usage

evalclust(data, clusters)

Arguments

data

A data.frame or a matrix with cases in rows and variables in colums.

clusters

A data.frame or a list of cluster memberships in a form of a sequence from the two-cluster solution to the maximal-cluster solution.

Value

The function returns a list with two components.

The eval component contains seven evaluation criteria in as vectors in a list. Namely, Within-cluster mutability coefficient (WCM), Within-cluster entropy coefficient (WCE), Pseudo F Indices based on the mutability (PSFM) and the entropy (PSFE), Bayessian (BIC) and Akaike (AIC) information criteria for categorical data and the BK index. To see them all in once, the form of a data.frame is more appropriate.

The opt component is present in the output together with the eval component. It displays the optimal number of clusters for the evaluation criteria from the eval component, except for WCM and WCE, where the optimal number of clusters is based on the elbow method.

References

Sulc Z., Cibulkova J., Prochazka J., Rezankova H. (2018). Internal Evaluation Criteria for Categorical Data in Hierarchical Clustering: Optimal Number of Clusters Determination, Metodoloski Zveski, 15(2), p. 1-20.

See Also

nomclust, nomprox, eval.plot.

Examples

# NOT RUN {
# sample data
data(data20)

# creating an object with results of hierarchical clustering
hca.object <- nomclust(data20, measure = "iof", method = "average", clu.high = 7)

# the cluster memberships
data20.clu <- hca.object$mem

# obtaining evaluation criteria for the provided dataset and cluster memberships
data20.eval <- evalclust(data20, clusters = data20.clu)



# }