nomclust (version 0.91.1010)

nomclust: Nominal Clustering

Description

The Nominal Clustering (nomclust) performs hierarchical cluster analysis (HCA) with objects characterized by nominal (categorical) variables. It performs a serie of cluster solutions, usually from two-cluster solution till six-cluster solution. It allows to choose one from 11 different similarity measures and one from 3 linkage methods. The function also contains an evaluation part. The created clusters are evaluated from a point of view of the within-cluster variability by the following indices: Within-cluster Mutability coefficient (WCM), Within-cluster entropy coefficient (WCE), Pseudo tau coefficient (PSTau), Pseudo uncertainty coefficient (PSU) and Pseudo F Indices based on the mutability (PSFM) and the entropy (PSFE).

Usage

nomclust(data, measure = iof, clu_low = 2, clu_high = 6, eval = TRUE,
  prox = FALSE, method = "complete")

Arguments

data

data frame or a matrix with cases in rows and variables in colums. Cases are characterized by nominal (categorical) variables coded as numbers.

measure

character string defining the similarity measure which wil be used for computation of proximity matrix: "eskin", "good1", "good2", "good3", "good4", "iof", "lin", <

clu_low

numeric value expressing the lower bound for number of cluster solutions.

clu_high

numeric value expressing the higher bound for number of cluster solutions.

eval

logical operator; if TRUE, there is performed an evaluation of clustering results

prox

logical operator; if TRUE, the proximity matrix is a part of the output

method

character string defining the clustering method. The following methods can be used: "average", "complete", "single".

Value

• Function returns a list following components: mem data frame consisting of cluster membership variables eval data frame containing clustering evaluation statistics prox matrix containing proximities between all combination of pairs of objects (voluntary)

See Also

evalclust, agnes.

Examples

#sample data
data(data20)
hca <- nomclust(data20, iof, method = "average", clu_high = 5, prox = TRUE)
#getting evaluation statistics
eval <- hca$eval
#getting cluster membership variables
mem <- hca$mem
#getting a proximity matrix
prox <- hca$prox