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K-means cluster analysis uses an algorithm that minimizes the within sum of squares to identify clusters of data points.
Standard k-means clustering groups objects into a pre-defined number of k classes without feature selection such that the within sum of squares is minimized.
Missing values ≤25% within sum scores of PTSD and HSCL-8 were resolved through calculations of mean scores of the remaining items.
The ratio of between sum of squares to within sum of squares (bss/wss) criteria was used for feature selection performed within each CV fold.
The optimal number of clusters is determined by iteratively performing the clustering process with each value of K. Within sum of square errors for each cluster is calculated and plotted in Figure 3A.
The formula of Wilks' Λ is given as (2) Λ = Π 1 (1 + λ k ), where λ k 's are the eigenvalues of the matrix S (= E−1 H), and E is within sum of squares matrix (sample covariance matrix) and H is between sum of squares matrix.
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We further adapted this idea of identifying differentially correlated networks by considering those with a large between-to-within sum of squares (BSS/WSS) ratio for the correlation values over the groups of interest.
(C) The best number of clusters was estimated by plotting the within-sum of squares against the possible number of clusters.
In order to calculate the F statistic, we first calculated the total mean, the within-group means, the within-group sum of squares (sswithin) and the between-group sum of squares (ssbetween).
end{aligned} Roughly, the number of clusters can be suggested by looking the bend point on the scree plot of within-cluster sum of square (the change in within-groups sum of square error below this point should be negligible)[18 21].
The idea in this method is to find a clustering (or grouping) of the observations so as to minimize the total within-cluster sums of squares.
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