Description Usage Arguments Details References Examples
This function performs a bootstrap ensemble hierarchical clustering of categorical data, as described in details below.
1 | Benhc(x, En)
|
x |
A nxp data matrix or data frame; n is the number of observations and p is the number of dimensions. |
En |
Number of clusterings to include in the ensemble, i.e., cardinality of the ensemble. |
The function 'Benhc' generates a dissimilarity matrix via the bootstrap ensemble. The ensembled dissimilarity matrix is generated using the same procedure as described for the function ‘enhc’ except that each clustering is based on a bootstrap sample of the data. The number of clusters for each clustering is selected randomly from {2,...,sqrt(n)}.
Amiri, S., Clarke, B., and Clarke, J. (2015). Clustering categorical data via ensembling dissimilarity matrices. arXiv preprint arXiv:1506.07930.
1 2 3 4 5 6 7 8 9 10 | #data('zoo')
### zoo includes the zoo data downloaded from UCI
### Machine Learning Repository
### Calculate ensemble dissimilarities with 150 ensemble members
#disten<-Benhc(zoo$obs,En=150)
### This function performs a hierarchical cluster analysis using
### dissimilarities obtained by the ensembling procedure in Benhc
#en<-hclust(disten,method='average')
### A plot of the dendrogram can be generated by
#plot(en,label=zoo$lab)
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Loading required package: dendextend
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Welcome to dendextend version 1.12.0
Type citation('dendextend') for how to cite the package.
Type browseVignettes(package = 'dendextend') for the package vignette.
The github page is: https://github.com/talgalili/dendextend/
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Attaching package: 'dendextend'
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Attaching package: 'ggdendro'
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