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I am working on cluster analysis of a completely categorical data set using package klaR and function kmodes. A sample of the data is available on dropbox. Just cross the sign-up notification dropbox will show when link opens.

The code to do the clustering was simple enough.

require(klaR)    
c1 <- kmodes(df, 5, 5, weighted = FALSE)

My questions are:

  1. How do I visualize these clusters? I have done simple plots in the past with k-means clusters: plotcluster(data, clus$cluster). When I try this here, I get:

     Error: is.numeric(x) || is.logical(x) is not TRUE
    
  2. How do I decide optimal number of clusters? I've read through Cluster analysis in R: determine the optimal number of clusters on Stack Overflow, but there is no mention of categorical variables anywhere and I could not understand which of the several methods discussed by the author will be applicable in my case.

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  • $\begingroup$ I'll suggest use clusplot. library("cluster"); clusplot(bob3, clus$cluster, color=TRUE, shade=TRUE, labels=2, lines=0). $\endgroup$ – sdrokr Dec 5 '18 at 13:19
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I think you need the command plot(data, col=clus\$cluster) instead. Or rather just plot(data[,c(j,l)], col=clus$cluster). This will give the graphs of a group of columns with respect to the clusters. About optimal number of clusters, I would just try different number of clusters starting from 2 and try to see from there how good I can do.

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