Try the flow cytometry clustering functions in Bioconductor.
-thomas
On Thu, Aug 11, 2011 at 7:07 AM, Ken Hutchison <vicvoncas...@gmail.com> wrote:
> Hello all,
> I am using the clustering functions in R in order to work with large
> masses of binary time series data, however the clustering functions do not
> seem able to fit this size of practical problem. Library 'hclust' is good
> (though it may be sub par for this size of problem, thus doubly poor for
> this application) in that I do not want to make assumptions about the number
> of clusters present, also due to computational resources and time hclust is
> not functionally good enough; furthermore k-means works fine assuming the
> number of clusters within the data, which is not realistic. The silhouette
> functions in 'Pam' and 'Clara' and (if I remember correctly) 'cluster' seem
> to be really bad through very thorough experimentation of data generation
> with known clusters. I am left then with either theoretical abstractions
> such as pruning hclust trees with minimal spanning trees or perhaps
> hand-rolling a hierarchical k-medoids which works extremely efficiently and
> without cluster number assumptions. Anybody have any suggestions as to
> possible libraries which I have missed or suggestions in general? Note: this
> is not a question for 'Bigkmeans' unless there exists a
> 'findbigkmeansnumberofclusters' function also.
> Thank you in advance for your
> assistance,
> Ken
>
> [[alternative HTML version deleted]]
>
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--
Thomas Lumley
Professor of Biostatistics
University of Auckland
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