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WPGMA

Machine Learning Algorithm

WPGMA, short for Weighted Pair Group Method with Arithmetic Mean, is a simple agglomerative, or bottom-up, hierarchical clustering method generally attributed to Sokal and Michener. Starting from a table of pairwise distances, it repeatedly merges the two closest remaining clusters and recalculates distances from the new cluster to every other cluster using an arithmetic mean of the merged clusters' previous distances, building a rooted tree, or dendrogram; because this averaging step gives equal weight to the two merged clusters regardless of how many original items each contains, it gives differential weight to the earlier, individual distances and is sensitive to differences in cluster size in a way its close relative, the unweighted UPGMA method, is not. Like UPGMA, WPGMA produces an ultrametric tree in which every original item is equally distant from the root, a property that implements a molecular-clock assumption of constant evolutionary rate and makes the method a common choice for building phylogenetic trees in molecular evolution studies. This description is adapted from Wikipedia contributors under CC BY-SA 4.0; changes were made. https://creativecommons.org/licenses/by-sa/4.0/

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Source Wikipedia: WPGMA
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Wikipedia: WPGMA
  • Lead section
    WPGMA (Weighted Pair Group Method with Arithmetic Mean) is a simple agglomerative (bottom-up) hierarchical clustering method, generally attributed to Sokal and Michener.
  • In Field: Machine Learning, Lead sentence
    WPGMA (Weighted Pair Group Method with Arithmetic Mean) is a simple agglomerative (bottom-up) hierarchical clustering method, generally attributed to Sokal and Michener.
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Wikidata: WPGMA
Wikidata Q28058996, class allow-list match (w-wdresolver-0926)View the Source
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