Optimal Variable Weighting A useful tool for ultrametric and additive tree clustering
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Optimal Variable Weighting Tags

Cluster additive synthesizer additive checker additive additive synth additive synthesis additive rhythm digital additive synthesizer additive tree topology

Optimal Variable Weighting Description

The OVW application performs optimal variable weighting for ultrametric and additive tree clustering as well as for K-means partitioning, following the method proposed by De Soete (1986, 1988), and Makarenkov and Legendre (2001). Given a rectangular data matrix Y (n,m), containing measurements of n objects on m variables, the procedure computes variable weights w(m) such that the resulting matrix of inter-object dissimilarities D(n,n) obtained from Y optimally satisfies either the ultrametric or the additive inequality, or optimally corresponds to a K-means partition with fixed number of groups K. The weights w are constrained to be nonnegative and their sum is equal to one. Take Optimal Variable Weighting for a test drive to fully assess its capabilities!

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