Abstract:
Obtaining a reasonable upper bound of the recovery rate of
an arbitrary clustering algorithm is of importance when exploring clus-
tering algorithms with respect to possible recovery rates. This paper
estimates the best possible recovery rate of an arbitrary clustering algo-
rithm with respect to any given input data set, based on two hypotheses.
For an example of a reasonably complex data set, obtained results are
veri ed and adjusted using a data visualization system.
Description:
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