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WotanClient 3.12.0..rarQ:
How to extract k-means clusters from hierarchical clustering
I have some genes that have the same functionality. I have a clustering which grouped them into a hierarchy, and I want to extract the groups which have similar functionality.
I want to extract a list of nodes from the hierarchy which are essentially a cluster of the original data. I have found out that kmeans can achieve this, but the problem is, it requires the original clustering to be strictly hierarchical, and my clustering is not hierarchical.
Does anyone know of any way to extract clusters from the hierarchy?
A:
Since the algorithm has not been implemented for general hierarchical clustering, it is easier to start from a hierarchical clustering.
Let’s consider the case where the clustering is hierarchical:
The sequence clustering algorithm starts from an initial clustering by putting each sequence into its own cluster, and repeatedly merges the clusters so that overall the clustering has as few clusters as possible. The resulting clustering will be a hierarchy.
In our case, this is not enough, as, for example, the cluster with the sequences
1
2
3
4
5
6
is “more different” from the cluster with the sequences
1
2
3
4
5
7
than we’d like. We would need to merge the two clusters so as to reduce the difference.
Now imagine that we use the cluster as a basis for another hierarchical clustering. As long as the sequences do not change between the two hierarchical clusterings, the cluster will be the same.
A simple way to merge the clusters is to start a new clustering by merging the two clusters in the original hierarchy. You can then apply the same algorithm as before.
A:
This seems to be the easiest way to extract a hierarchical clustering of the individual clusters after k-means:
Divide the data into clusters (k-means)
Define new centroids based on the mean of all sequences in each cluster
Recombine the clusters using the new centroids
Code below:
import numpy as np
from sklearn.cluster import KMeans
from sklearn.cluster import MiniBatchKMeans
from sklearn.model_selection import train_test_split
from scipy import
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