[R] How to reduce the sparseness in a TDM to make a cluster plot readable?

[Jim Lemon]

Hi Andrew,
>From your last email the answer to your problem may be the
findFreqTerms() function. Just increase the number of times a term has
to appear and check the result until you get the matrix size that you
want.

Jim

On Fri, Sep 18, 2020 at 5:32 PM Andrew <phaedrusv using gmail.com> wrote:
>
> Hi Abby
>
> Many thanks for reaching out with an offer of help. Very much appreciated.
>
> (1) The packages I'm using are 'tm' for text-mining and the TDM and for
> the clustering it is 'cluster'
> (2) Not sure where the problem is happening as it doesn't show up as an
> error. Where it manifests is in the plotting, however logic would
> suggest that it concerns the removal of sparse terms, so that would be
> in the TDM process
> (3) I don't think I can provide a reproducible example. When I practice
> using data sets that packages provide, all is fine. The trouble is when
> I apply it to my own data sets which are five documents, etc., as described.
>
> I think the nub of it is really to find a way that I can subset the TDM
> to return the twenty or thirty most frequently used words, and then to
> plot those using hclust. However, when searching on-line I haven't been
> able to find any suggestions on how to do that, nor is there any mention
> of using that approach in the books and tutorials I have.
>
> If you (or someone on this list) can advise on how I can sort the terms
> in the TDM from most to least frequent, and then to subset the top
> twenty or thirty most frequently occurring terms (preferably using tf as
> well as tf-idf) and then I can plot that sub-set, then I think that that
> would do the trick, and the terms would be plotted clearly and legibly.
>
> Thanks again for your offer of help. I hope that my reply helps clarify
> rather than muddy the situation.
>
> Best wishes
> Andy
>
>
> On 17/09/2020 08:43, Abby Spurdle wrote:
> > I'm not familiar with these subjects.
> > And hopefully, someone who is, will offer some better suggestions.
> >
> > But to get things started, maybe...
> > (1) What packages are you using (re: tdm)?
> > (2) Where does the problem happen, in dist, hclust, the plot method
> > for hclust, or in the package(s) you are using?
> > (3) Do you think you could produce a small reproducible example,
> > showing what is wrong, and explaining you would like it to do instead?
> >
> > Note that if the problem relates to hclust, or the plot method, then
> > you should be able to produce a much simpler example.
> > e.g.
> >
> >      mycount.matrix <- matrix (rpois (25000, 20),, 5)
> >      head (mycount.matrix, 3)
> >      tail (mycount.matrix, 3)
> >
> >      plot (hclust (dist (mycount.matrix) ) )
> >
> > On Tue, Sep 15, 2020 at 6:54 AM Andrew <phaedrusv using gmail.com> wrote:
> >> Hello all
> >>
> >> I am doing some text mining on a set of five plain text files and have
> >> run into a snag when I run hclust in that there are just too many leaves
> >> for anything to be read. It returns a solid black line.
> >>
> >> The texts have been converted into a TDM which has a dim of 5,292 and 5
> >> (as per 5 docs).
> >>
> >> My code for removing sparsity is as follows:
> >>
> >>   > tdm2 <- removeSparseTerms(tdm, sparse=0.99999)
> >>
> >>   > inspect(tdm2)
> >>
> >> <<TermDocumentMatrix (terms: 5292, documents: 5)>>
> >> Non-/sparse entries: 10415/16045
> >> Sparsity           : 61%
> >> Maximal term length: 22
> >> Weighting          : term frequency (tf)
> >>
> >> While the tf-idf weighting returns this when 0.99999 sparseness is removed:
> >>
> >>   > inspect(tdm.tfidf)
> >> <<TermDocumentMatrix (terms: 5292, documents: 5)>>
> >> Non-/sparse entries: 7915/18545
> >> Sparsity           : 70%
> >> Maximal term length: 22
> >> Weighting          : term frequency - inverse document frequency
> >> (normalized) (tf-idf)
> >>
> >> I have experimented by decreasing the value I use for decreasing
> >> sparseness, and that helps a bit, for example:
> >>
> >>   > tdm2 <- removeSparseTerms(tdm, sparse=0.215)
> >>   > inspect(tdm2)
> >> <<TermDocumentMatrix (terms: 869, documents: 5)>>
> >> Non-/sparse entries: 3976/369
> >> Sparsity           : 8%
> >> Maximal term length: 14
> >> Weighting          : term frequency (tf)
> >>
> >> But, no matter what I do, the resulting plot is unreadable. The code for
> >> plotting the cluster is:
> >>
> >>   > hc <- hclust(dist(tdm2, method = "euclidean"), method = "complete")
> >>   > plot(hc, yaxt = 'n', main = "Hierarchical clustering")
> >>
> >> Can someone kindly either advise me what I am doing wrong and/ or
> >> signpost me to some detailed info on how to fix this.
> >>
> >> Many thanks in anticipation.
> >>
> >> Andy
> >>
> >>
> >>          [[alternative HTML version deleted]]
> >>
> >> ______________________________________________
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>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.