Wolfgang,

Maybe relevant -
H. SPÄTH: Anticlustering: Maximizing the Variance Criterion. Control and
Cybernetics 15, 213 - 218 (1986).

Fionn Murtagh



Hi:
For some time I'm working on a problem of sampling a set of K
observations (cases) from a large data set with N >> K cases so that
the selected observations are as "different as possible". In more
mathematical terms, I'm interested in locating those K cases which
will result in a (not necessarily Euclidean) distance matrix in which
the smallest off-diagonal entry d_ij is as large as possible.

I have developed an algorithm which seems to work very well and
generates sets which are either optimal or close to optimality without
computing the entire distance matrix. However, I'm thinking more
and more that this maybe a known problem to people who work in
Cluster Analysis, MDS, or classification. I wonder if anybody on
this list could point me to some references about this search
problem.

Thanks, Wolfgang Hartmann




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