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Unsupervised clustering analysis using a Euclidean distance and the ward.D2 linkage method was performed.
The Ward's linkage method was used in this analysis.
Between group average linkage method was applied, and rescaled distance was selected as measurement.
The average linkage method was used to build the dendrogram, which represented the nested correlation structure of all AE events.
A classification scheme using the Euclidean distance for similarity measures and the between-groups linkage method was performed on the normalized data to produce the most distinctive classification where each member within a group is more similar to its fellow members than to any member outside of the group (Guler and Thyne 2004).
The distance matrix was 1-correlation and linkage method was average.
Similar(28)
Clustering using cosine distance and the Ward's linkage method were performed using the 'Heatmap' function of the ComplexHeatmap package.
Two typical clustering methods: k-means and completed linkage method are employed to divide the (k-1) dimensional diffusion map into k clusters.
In addition, the calculation speed for the complete linkage method is 3-fold enhanced than that for the k-means method (data not shown).
Since the complete linkage method provides higher modularity, higher speed, and less CVs of cluster size than the k-means method, the complete linkage method is selected for clustering the diffusion map of the PPI network.
The Euclidean distance metric and average linkage method were used to carry out the hierarchical clustering analysis.
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