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Re: Data clustering
From: |
CdeMills |
Subject: |
Re: Data clustering |
Date: |
Mon, 23 Jun 2014 08:06:31 -0700 (PDT) |
Juan Pablo Carbajal-2 wrote
> I hva edone similr stuff in very different ways. For examlpe a direct
> use of diff can do it or running averages of the L2 norm derivative on
> the smoothed output, even moving histrograms can help you decide when
> you are stable. It all depends a lot on the time scale.
> If you want to go data driven, you can try kmeans and then detect the
> outliers from each cluster (they probably will be transitional
> values). Try also the soft partition fcm in the fuzzy logic package.
> Can you send a example data or plot?
Hello,
I enclose an example. There system is launched at 100% with a soft-start
mechanism. After allowing time to stabilisation, the power is reduced by
steps of 10 %, then put back at 100%. The enclosed file contains: "Signal";
the raw data; "indt", the start and end of each stable period; and Sshort, a
shortened version with only the stable segments.
The end of the stable parts are at (rounded); 39 48 58 68 79
87 97 107
If I do
[idx, center]=kmeans(Sshort,8);
I get (sorted and rounded) : 39 48 58 75 92 107 107 108
The strange point is that the last segment is repeated thrice.
data.data <http://octave.1599824.n4.nabble.com/file/n4664952/data.data>
Any idea on how to automatically cluster such kind of data ?
Regards
Pascal
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