On Mar 5, 2013, at 10:10 AM, Olivier Grisel wrote:
> This code is in C++ and the scikit-learn core maintainers are not all
> experts in C++ and prefer cython for optimized code.
>
> A cython rewrite of some of those algorithms would be of interest though.
For anyone interested in either reimplementing the fastcluster routines in
cython or
implementing the algorithms from scratch, Muller's accompanying paper, "Modern
hierarchical, agglomerative clustering algorithms", is worth reading.
> This paper presents algorithms for hierarchical, agglomerative clustering
> which
> perform most efficiently in the general-purpose setup that is given in modern
> standard software. Requirements are: (1) the input data is given by pairwise
> dissimilarities between data points, but extensions to vector data are also
> discussed
> (2) the output is a “stepwise dendrogram”, a data structure which is shared by
> all implementations in current standard software. We present algorithms (old
> and
> new) which perform clustering in this setting efficiently, both in an
> asymptotic
> worst-case analysis and from a practical point of view. The main
> contributions of
> this paper are: (1) We present a new algorithm which is suitable for any
> distance
> update scheme and performs significantly better than the existing algorithms.
> (2)
> We prove the correctness of two algorithms by Rohlf and Murtagh, which is
> necessary
> in each case for different reasons. (3) We give well-founded recommendations
> for the
> best current algorithms for the various agglomerative clustering schemes.
http://arxiv.org/abs/1109.2378
-Robert
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