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Below root-level, pairs of clusters are collapsed if and only if doing so improves the median split silhouette.
However, the k-medoids algorithm offers the possibility to select the number of clusters according to the Median Split Silhouette or MSS [27].
In this algorithm, the number of clusters appropriate in the final clustering solution is determined automatically according the median split silhouette criterion [ 80].
The clustering algorithm built a hierarchical tree by recursively partitioning or collapsing clusters at each level, using MSS (Median Split Silhouette) criteria to identify the level of the tree with maximally homogeneous clusters [ 61].
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Using HOPACH, motifs highlighted by our analyses were assigned to separate groups, based upon correspondence of their k-mer scores and the Median/Mean Split Silhouette (MSS) criterion [53].
†Perceived stress in mothers categorised according to a median split.
Participants were divided by median split on an Expectation of Success scale.
Participants in each group were subdivided by trait body dissatisfaction level, based on a median split.
‡Sleep duration categorised according to a median split and rounded to the nearest hour.
Without loss of generality, we will consider this median split in the present study.
The results using this approach (Table S2) were consistent with the median split composite results.
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