Re: [scikit-learn] Any clustering algo to cluster multiple timing series data?
How about scaling data first by MinMaxScaler and then cluster? What I thought is scaling can scale then into 0~1 section, and it can ignore the quantity of each data After scaling, it shows the increasing/decreasing ratio between each points. Then cluster then by the eucledian distance should work? ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] Any clustering algo to cluster multiple timing series data?
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 wrote: > > You can use it to get a single similarity / closeness number between two > timeseries and then feed that into a clustering algorithm. > > > > For instance look at > > https://github.com/markdregan/K-Nearest-Neighbors-with-Dynamic-Time-Warping > > > > > > as a first idea: > > if you expand the distance function d = lambda x,y: abs(x-y) to a > multivariate local distance > > > > d2 = lambda a,b: np.sqrt(float((a[0]-b[0])**2 + (a[1]-b[1])**2) > > (or any other n-dim metric) > > > > Then you have an algorithm that could cluster the timeseries. > > > > It does also work when the timeseries are of equal length… > > > > Best > > Mikkel Brynildsen > > > > > > From: scikit-learn > On Behalf Of lampahome > Sent: 17. januar 2019 08:45 > To: Scikit-learn mailing list > Subject: Re: [scikit-learn] Any clustering algo to cluster multiple timing > series data? > > > > > > > > Mikkel Haggren Brynildsen 於 2019年1月17日 週四 下午3:07寫道： > > What about dynamic time warping ? > > > > I thought DTW is used to different length of two datasets > > But I only get the same length of two datasets. > > Maybe it doesn't work? > > > > > > ___ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] Any clustering algo to cluster multiple timing series data?
You can use it to get a single similarity / closeness number between two timeseries and then feed that into a clustering algorithm. For instance look at https://github.com/markdregan/K-Nearest-Neighbors-with-Dynamic-Time-Warping as a first idea: if you expand the distance function d = lambda x,y: abs(x-y) to a multivariate local distance d2 = lambda a,b: np.sqrt(float((a[0]-b[0])**2 + (a[1]-b[1])**2) (or any other n-dim metric) Then you have an algorithm that could cluster the timeseries. It does also work when the timeseries are of equal length… Best Mikkel Brynildsen From: scikit-learn On Behalf Of lampahome Sent: 17. januar 2019 08:45 To: Scikit-learn mailing list Subject: Re: [scikit-learn] Any clustering algo to cluster multiple timing series data? Mikkel Haggren Brynildsen mailto:mbrynild...@grundfos.com>> 於 2019年1月17日 週四 下午3:07寫道： What about dynamic time warping ? I thought DTW is used to different length of two datasets But I only get the same length of two datasets. Maybe it doesn't work? ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] Any clustering algo to cluster multiple timing series data?
Mikkel Haggren Brynildsen 於 2019年1月17日 週四 下午3:07寫道： > What about dynamic time warping ? > I thought DTW is used to different length of two datasets But I only get the same length of two datasets. Maybe it doesn't work? ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] Any clustering algo to cluster multiple timing series data?
What about dynamic time warping ? Sendt fra min iPhone > Den 17. jan. 2019 kl. 05.31 skrev lampahome : > > Cluster algo cluster samples by calculating the euclidean distance. > I wonder if any clustering algo can cluster the timing series data? > > EX: > Every items has there sold numbers of everyday. > Item,Day1,Day2,Day3,Day4,Day5 > A,1,5,1,5,1 > B,10,50,10,50,10, > C,4,70,30,10,50 > > The difference ratio of A and B are 500%,20%,500%,20%, > I want to make A be the same cluster, C is another one. > > If I don't want to calculate the difference ratio of each samples > > Is there anyway to cluster by the difference ratio of each samples? > > thx > ___ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
