Hclust (d, method = "complete", Members=null)
D is the distance matrix.
Method represents the merging methods of the class, which are:
Single Shortest distance method
Complete the longest distance method
Median intermediate distance method
McQuitty Similarity method
Average class averaging method
Centroid Center of gravity method
Ward Deviation squared sum method
# ' Example
D <-Dist (x)
HC <-Hclust (d, "single")
Plot (HC)
# ' Then you can use Rect.hclust (tree, k = null, which = NULL, x = null, h = Null,border = 2, cluster = NULL) to determine the number of classes. # ' tree is the object of the finding. K is the number of classifications, and H is the threshold for the distance between classes. Border are painted colors that are used for classification.
Rect.hclust (hc,k=2)
Rect.hclust (hc,h=0.5)
# ' Extract the categories to which each sample belongs
Label <-Cutree (hc,k=2)
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