[R] R-Package for Recursive Partitioning without Classification or Regression

[Achim Zeileis]

On Fri, 28 Jul 2017, Tom D. Harray wrote:

> Hello,
>
> I have a question related to recursive partitioning, but I cannot find
> an answer, likely because I don't know how to properly word my Google
> search query.

I think you are looking for "divisive hierarchical clustering" which is 
the more commonly used term for clustering based on recursive 
partitioning. The classical implementation for this would be diana() from 
the "cluster" package. The "Cluster" task view at 
https://CRAN.R-project.org/view=Cluster also lists "isopam" and 
http://search.r-project.org/cgi-bin/namazu.cgi?query=divisive+clustering&idxname=functions&idxname=views
also gives a few further leads.

> All recursive partitioning examples, which I can find, are used for
> either classification or regression trees like
>
>   library(tree)
>   data(iris)
>   tree(Species ~ Sepal.Width + Petal.Width, data = iris)
>
> which implies building a model. However, I would like to split data
> like clustering similar to decision tree methods, because I have
> nothing to predict.

But do you want to split along observed variables or not? If not, then 
you're in a unsupervised clustering situation (see my comment above). But 
if you want to split along covariates, then this is supervised and you're 
possibly looking for a multivariate regression tree?

Hope that helps,
Z

> My question is: Is there a package, which I can use to partition my
> data without classification or regression so that it resembles
> clustering methods?
>
>
> Thanks and regards,
>
> Dirk
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>