# Clustering values by their proximity in python (machine learning?) [duplicate]

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### Question :

Clustering values by their proximity in python (machine learning?) [duplicate]

I have an algorithm that is running on a set of objects. This algorithm produces a score value that dictates the differences between the elements in the set.

The sorted output is something like this:

[1,1,5,6,1,5,10,22,23,23,50,51,51,52,100,112,130,500,512,600,12000,12230]

If you lay these values down on a spreadsheet you see that they make up groups

[1,1,5,6,1,5] [10,22,23,23] [50,51,51,52] [100,112,130] [500,512,600] [12000,12230]

Is there a way to programatically get those groupings?

Maybe some clustering algorithm using a machine learning library? Or am I overthinking this?

I’ve looked at scikit but their examples are way too advanced for my problem…

# Don’t use clustering for 1-dimensional data

Clustering algorithms are designed for multivariate data. When you have 1-dimensional data, sort it, and look for the largest gaps. This is trivial and fast in 1d, and not possible in 2d. If you want something more advanced, use Kernel Density Estimation (KDE) and look for local minima to split the data set.

There are a number of duplicates of this question:

A good option if you don’t know the number of clusters is `MeanShift`:

``````import numpy as np
from sklearn.cluster import MeanShift, estimate_bandwidth

x = [1,1,5,6,1,5,10,22,23,23,50,51,51,52,100,112,130,500,512,600,12000,12230]

X = np.array(zip(x,np.zeros(len(x))), dtype=np.int)
bandwidth = estimate_bandwidth(X, quantile=0.1)
ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)
ms.fit(X)
labels = ms.labels_
cluster_centers = ms.cluster_centers_

labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)

for k in range(n_clusters_):
my_members = labels == k
print "cluster {0}: {1}".format(k, X[my_members, 0])
``````

Output for this algorithm:

``````cluster 0: [ 1  1  5  6  1  5 10 22 23 23 50 51 51 52]
cluster 1: [100 112 130]
cluster 2: [500 512]
cluster 3: 
cluster 4: 
cluster 5: 
``````

Modifying `quantile`variable you can change the clustering number selection criteria

You can use clustering to group these. The trick is to understand that there are two dimensions to your data: the dimension you can see, and the “spatial” dimension that looks like [1, 2, 3… 22]. You can create this matrix in numpy like so:

``````import numpy as np

y = [1,1,5,6,1,5,10,22,23,23,50,51,51,52,100,112,130,500,512,600,12000,12230]
x = range(len(y))
m = np.matrix([x, y]).transpose()
``````

Then you can perform clustering on the matrix, with:

``````from scipy.cluster.vq import kmeans
kclust = kmeans(m, 5)
``````

kclust’s output will look like this:

``````(array([[   11,    51],
[   15,   114],
[   20, 12115],
[    4,     9],
[   18,   537]]), 21.545126372346271)
``````

For you, the most interesting part is the first column of the matrix, which says what the centers are along that x dimension:

``````kclust[:, 0]
# [20 18 15  4 11]
``````

You can then assign your points to a cluster based on which of the five centers they are closest to:

``````assigned_clusters = [abs(cluster_indices - e).argmin() for e in x]
# [3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 2, 2, 2, 2, 1, 1, 0, 0, 0]
``````