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

Question

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...

Answer 1

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:

• 1D Number Array Clustering
• Cluster one-dimensional data optimally?