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The k-means method is a simple, non-parametric clustering method that minimizes within-cluster variability and maximizes between cluster variability.
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In particular, we find that in the absence of mutation the strain that maximizes between-host fitness can outcompete all other strains.
In this paper, we propose a Multiple-Instance Feature Extraction (MIFE) framework to design algorithms at both the bag and instance levels based on the Maximum Trace-Difference criterion, which simultaneously maximizes between-class scattering and minimizes within-class scattering.
This transformation does not affect within-population variation, but maximizes between-population variation.
This method maximizes between- and minimizes within-group variability by conducting a principal components analysis on genetic data, before submitting those principal components to a discriminant analysis.
dCluster analysis classifies individuals on the basis of similarities of some characteristics seeking to minimize within-group variance and maximize between-group variance.
On the other hand, discriminant analysis-based methods maximize between-class variance and minimize within-class variance to separate neighboring categories with maintenance of their ordinal scales.
Second, ASFDA seeks projections to minimize within-class temporal variation and maximize between-class temporal variation simultaneously based on maximum margin criterion.
The cluster analysis reduces n original estuaries into g groups such that 1<g<n with the general goal of minimizing within-group variation and <span class="lh lhl">maximizing between-group variation.
Accurate stand delineation has the goal of maximizing between-stand variance while minimizing within-stand variance.
At the same time, trees aim to discriminate disjunctive homogeneous subsets by minimizing within-variance and maximizing between-variance.
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