Duong Trung Nghia - 4 months ago 10x

Python Question

Let assume that we have X matrix as follows:

`[[9 0]`

[1 4]

[2 3]

[8 5]]

Then,

`from scipy.cluster.hierarchy import linkage`

Z = linkage(X, method="ward")

print(Z)

The returning matrix is follows:

`[[ 1. 2. 1.41421356 2. ]`

[ 0. 3. 5.09901951 2. ]

[ 4. 5. 10. 4. ]]

What is the meaning of the returning values?

Answer

Although this has been answered before, it was a "read the docs" answer. I think it is useful to explain the docs a bit.

From the docs, we read that:

An (n−1) by 4 matrix Z is returned. At the i-th iteration, clusters with indices Z[i, 0] and Z[i, 1] are combined to form cluster n + i. A cluster with an index less than n corresponds to one of the n original observations. The distance between clusters Z[i, 0] and Z[i, 1] is given by Z[i, 2]. The fourth value Z[i, 3] represents the number of original observations in the newly formed cluster.

I think the confusing part is the the first n clusters are singletons ("original observations"). So the first value in Z actually the n+1'th cluster. It is the first cluster to combine two singletons.

So in your example, Z[0] is the 4+1'th cluster. We have

```
Z[0] = [ 1. 2. 1.41421356 2. ]
```

The first two values tell us which clusters were used to create cluster Z[0]. They are cluster_1, the singleton [1,4], and cluster_2, the singleton [2, 3].

The third value gives us the distance between the clusters. We can verify that sqrt((2-1)^2 + (3-4)^2)) = 1.41...

The fourth value tells us how many singletons are in cluster Z[0].

So looking at your last cluster, Z[2], we see that is combines the firs two clusters in Z. Each of them contains two unique singletons, so the Z[2,3] = 4.

Source (Stackoverflow)

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