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WPGMA

WPGMA is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand WPGMA rather than just read about it. In short: WPGMA (Weighted Pair Group Method with Arithmetic Mean) is a simple agglomerative (bottom-up) hierarchical clustering method, generally attributed to Sokal and Michener. The WPGMA method is similar to its unweighted variant, the UPGMA method.

WPGMA — main illustration
WPGMA — illustration

Key takeaways

  • WPGMA belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect WPGMA to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of WPGMA from memory before moving on to harder problems.

Reference excerpt

WPGMA (Weighted Pair Group Method with Arithmetic Mean) is a simple agglomerative (bottom-up) hierarchical clustering method, generally attributed to Sokal and Michener. The WPGMA method is similar to its unweighted variant, the UPGMA method.

Algorithm The WPGMA algorithm constructs a rooted tree (dendrogram) that reflects the structure present in a pairwise distance matrix (or a similarity matrix). At each step, the nearest two clusters, say i {\displaystyle i} and j {\displaystyle j} , are combined into a higher-level cluster i ∪ j {\displaystyle i\cup j} . Then, its distance to another cluster k {\displaystyle k} is simply the arithmetic mean of the average distances between members of k {\displaystyle k} and i {\displaystyle i} and k {\displaystyle k} and j {\displaystyle j} :

d ( i ∪ j ) , k = d i , k + d j , k 2 {\displaystyle d_{(i\cup j),k}={\frac {d_{i,k}+d_{j,k}}{2}}}

The WPGMA algorithm produces rooted dendrograms and requires a constant-rate assumption: it produces an ultrametric tree in which the distances from the root to every branch tip are equal. This ultrametricity assumption is called the molecular clock when the tips involve DNA, RNA and protein data.

Working example This working example is based on a JC69 genetic distance matrix computed from the 5S ribosomal RNA sequence alignment of five bacteria: Bacillus subtilis ( a {\displaystyle a} ), Bacillus stearothermophilus ( b {\displaystyle b} ), Lactobacillus viridescens ( c {\displaystyle c} ), Acholeplasma modicum ( d {\displaystyle d} ), and Micrococcus luteus ( e {\displaystyle e} ).

First step First clustering Let us assume that we have five elements ( a , b , c , d , e ) {\displaystyle (a,b,c,d,e)} and the following matrix D 1 {\displaystyle D_{1}} of pairwise distances between them :

In this example, D 1 ( a , b ) = 17 {\displaystyle D_{1}(a,b)=17} is the smallest value of D 1 {\displaystyle D_{1}} , so we join elements a {\displaystyle a} and b {\displaystyle b} .

First branch length estimation Let u {\displaystyle u} denote the node to which a {\displaystyle a} and b {\displaystyle b} are now connected. Setting δ ( a , u ) = δ ( b , u ) = D 1 ( a , b ) / 2 {\displaystyle \delta (a,u)=\delta (b,u)=D_{1}(a,b)/2} ensures that elements a {\displaystyle a} and b {\displaystyle b} are equidistant from u {\displaystyle u} . This corresponds to the expectation of the ultrametricity hypothesis. The branches joining a {\displaystyle a} and b {\displaystyle b} to u {\displaystyle u} then have lengths δ ( a , u ) = δ ( b , u ) = 17 / 2 = 8.5 {\displaystyle \delta (a,u)=\delta (b,u)=17/2=8.5} (see the final dendrogram)

First distance matrix update We then proceed to update the initial distance matrix D 1 {\displaystyle D_{1}} into a new distance matrix D 2 {\displaystyle D_{2}} (see below), reduced in size by one row and one column because of the clustering of a {\displaystyle a} with b {\displaystyle b} . Bold values in D 2 {\displaystyle D_{2}} correspond to the new distances, calculated by averaging distances between each element of the first cluster ( a , b ) {\displaystyle (a,b)} and each of the remaining elements:

… excerpt ends here. Continue reading the full article.

Illustrations

WPGMA illustration
WPGMA illustration
WPGMA illustration

Worked examples

Example 1 — a first encounter with WPGMA

Start with the simplest possible case. Write down what WPGMA claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to WPGMA before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about WPGMA ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of WPGMA

In research
WPGMA appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses WPGMA in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
WPGMA is common in secondary-school and first-year university syllabi. It links to neighbouring topics Bioinformatics algorithms, Cluster analysis algorithms, Computational phylogenetics, so understanding it makes those chapters shorter.
In everyday life
Look for WPGMA outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study WPGMA in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what WPGMA means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain WPGMA out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is WPGMA in simple terms?

WPGMA (Weighted Pair Group Method with Arithmetic Mean) is a simple agglomerative (bottom-up) hierarchical clustering method, generally attributed to Sokal and Michener. The WPGMA method is similar to its unweighted variant, the UPGMA method.

Why does WPGMA matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study WPGMA?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on WPGMA.

Tags

  • Bioinformatics algorithms
  • Cluster analysis algorithms
  • Computational phylogenetics

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