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based on proximity between training observations. Performance
comparisons are presented on synthetic and real examples versus
k-nearest neighbors, Fisher's linear discriminant and support vector
machines. We demonstrate that the proposed semiparametric classifier has
performance approaching that...
, a new clustering method that attempts to
find modes of a density by analyzing the minimal spanning tree of a sample.
The method exploits the connection between the minimal spanning tree and
nearest neighbor density (e.g. normal mixture) or about the geometric shapes
of the clusters, and is...
-estimators, and building clustering objective functions. Finally,
using the common thread of concavity, all three will be combined to build a
comprehensive, flexible procedure for robust cluster analysis.
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