问题
I have three matrices to compare. Each of them is 5x6. I originally wanted to use hierarchical clustering to cluster the matrices, such that the most similar matrices are grouped, given a threshold of similarity.
I could not find any such functions in python, so I implemented the distance measure by hand, (p-norm where p=2). Now I have a 3x3 distance matrix (which I believe is also a similarity matrix in this case).
I am now trying to produce a dendrogram. This is my code, and this is what is wrong. I want to produce a graph (a dendrogram if possible) that shows clusters of the matrices that are most similar. Of matrices 0,1,2, 0 and 2 are the same and should be clustered together first, and 1 is different.
The distance matrix looks like this:
> 0 1 2
0 0.0 2.0 3.85e-16
1 2.0 0.0 2.0
2 3.85e-16 2.0 0.0
Code:
from scipy.cluster.hierarchy import dendrogram
import matplotlib.pyplot as plt
import numpy as np
from scipy.cluster.hierarchy import linkage
mat = np.array([[0.0, 2.0, 3.8459253727671276e-16], [2.0, 0.0, 2.0], [3.8459253727671276e-16, 2.0, 0.0]])
dist_mat = mat
linkage_matrix = linkage(dist_mat, "single")
dendrogram(linkage_matrix, color_threshold=1, labels=["0", "1", "2"],show_leaf_counts=True)
plt.title=("test")
plt.show()
This is the output:
What is the meaning of the linkage(dist_mat, 'single')? I would have assumed the output graph to look something like this, where the distance is 2.0 between 0 and 1 (for example).
Are there better ways to represent these data? Is there a function that could take in several matrices instead of points, to compare and form a distance matrix, and then cluster? I am open to other suggestions on how to visualize the differences between these matrices.
回答1:
The first argument of linkage should not be the square distance matrix. It must be the condensed distance matrix. In your case, that would be np.array([2.0, 3.8459253727671276e-16, 2]). You can convert from the square distance matrix to the condensed form using scipy.spatial.distance.squareform
If you pass a two dimensional array to linkage with shape (m, n), it treats it as an array of m points in n-dimensional space and it computes the distances of those points itself. That's why you didn't get an error when you passed in the square distance matrix--but you got an incorrect plot. (This is an undocumented "feature" of linkage.)
Also note that because the distance 3.8e-16 is so small, the horizontal line associated with the link between points 0 and 2 might not be visible in the plot--it is on the x axis.
Here's a modified version of your script. For this example, I've changed that tiny distance to 0.1, so the associated cluster is not obscured by the x axis.
import numpy as np
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.spatial.distance import squareform
import matplotlib.pyplot as plt
mat = np.array([[0.0, 2.0, 0.1], [2.0, 0.0, 2.0], [0.1, 2.0, 0.0]])
dists = squareform(mat)
linkage_matrix = linkage(dists, "single")
dendrogram(linkage_matrix, labels=["0", "1", "2"])
plt.title("test")
plt.show()
Here is the plot created by the script:
来源：https://stackoverflow.com/questions/41416498/dendrogram-or-other-plot-from-distance-matrix