# [scikit-learn] Any clustering algo to cluster multiple timing series data?

Alexandre Gramfort alexandre.gramfort at inria.fr
Thu Jan 17 03:53:35 EST 2019

```you can have a look at :

https://tslearn.readthedocs.io/en/latest/

Alex

On Thu, Jan 17, 2019 at 9:01 AM Mikkel Haggren Brynildsen
<mbrynildsen at grundfos.com> wrote:
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> You can use it to get a single similarity / closeness number between two timeseries and then feed that into a clustering algorithm.
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> For instance look at
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> https://github.com/markdregan/K-Nearest-Neighbors-with-Dynamic-Time-Warping
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> as a first idea:
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> if you expand the distance function d = lambda x,y: abs(x-y) to a multivariate local distance
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> d2 = lambda a,b: np.sqrt(float((a[0]-b[0])**2 + (a[1]-b[1])**2)
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> (or any other n-dim metric)
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> Then you have an algorithm that could cluster the timeseries.
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> It does also work when the timeseries are of equal length…
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> Best
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> Mikkel Brynildsen
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> From: scikit-learn <scikit-learn-bounces+mbrynildsen=grundfos.com at python.org> On Behalf Of lampahome
> Sent: 17. januar 2019 08:45
> To: Scikit-learn mailing list <scikit-learn at python.org>
> Subject: Re: [scikit-learn] Any clustering algo to cluster multiple timing series data?
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> Mikkel Haggren Brynildsen <mbrynildsen at grundfos.com> 於 2019年1月17日 週四 下午3:07寫道：
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> What about dynamic time warping ?
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> I thought DTW is used to different length of two datasets
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> But I only get the same length of two datasets.
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> Maybe it doesn't work?
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