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I need help in the analyse of a categorization problem.

Given a set of **dates** (small set: 20 elements maximum), I would like to group dates which are equally distributed (with a tolerance).
It can be, for instance, monthly or weekly separated dates.

Here is an example. Given this repartition :

I would like to categorize into these two groups:

The problem is that I am a developer, not a data scientist. I have an intuition that it should be possible to do a kind of regression.

I have no clue how to analyse this problem. Can you help me with that, please ?

Cheers

PS: I have already seen this thread (Recurring events - finding in a time series) but I have not helped me.

1Why do you need to do a classification to group the dates? Will setting a tolerance and specifying an algorithm to separate the dates into two groups not be sufficient since the set of dates are small? – gchaks – 2017-07-26T20:33:41.240

That is true but the example I gave is a very simple one. Actually, I can have one or more groups of recurrent dates and some "noise" too (dates which are not recurrent). This said, the data set will be small. Do you know some algorithms that can do the job? Otherwise, I thought that algorithms like k-means work, but I am not sure. – Damien – 2017-07-27T10:08:51.767