Thanks a lot guys for your great hints.

I've tested to consider only the magnitude or only the phase to attain the
goal, but those don't work in my case, I should consider both
simultaneously to get a correct result. Also, I've considered converting
into two columns (imaginary + real columns). But, the problem is that after
bi-clustering, imaginary columns with their corresponding real columns can
be in different clusters and the problem arises how to assign them to a
similar clusters. What I'm saying is that for each complex value, most
likely real and imag part would be in different clusters and it's not easy
to retrieve them to be in a same cluster. What do you think? Is it possible
to modify Scikit-learn code to work with complex values? Or ...?

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On Sun, 20 Oct 2019 at 10:09, serafim loukas <seral...@hotmail.com> wrote:

> I would take the magnitude.
> Otherwise you will have to modify the source code to make it work with
> complex values.
>
> Bests,
> Makis
>
> On Oct 20, 2019, at 15:55, Fernando Marcos Wittmann <
> fernando.wittm...@gmail.com> wrote:
>
> 
> What about converting into two columns? One with the real projection and
> the other with the complex projection?
>
> On Sat, Oct 19, 2019, 3:44 PM ahmad qassemi <ahmadqass...@gmail.com>
> wrote:
>
>> Dear Mr/Mrs,
>>
>>  I'm a PhD student in DS. I'm trying to use your provided code on *Spectral
>> CoClustering *and *Spectral Biclustering* to bi-cluster my data matrix (
>> https://scikit-learn.org/stable/modules/biclustering.html). Since my
>> data has complex values, i.e., matrix elements are complex, your modules
>> don't work on my data. It seems that the reason is your K-means' code
>> doesn't work with complex numbers. I will really appreciate it if you take
>> some time and tell me how should I apply your codes on my complex data.
>> Thanks a lot in advance.
>>
>> Sincerely,
>> Ahmad Qassemi
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