Need help on FCM clustering

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Aiman Almazroey
Aiman Almazroey on 26 Nov 2011
Commented: Walter Roberson on 8 Dec 2016
Is any one knows how to create 5 clusters by using FCM please. I've number of samples across number of genes, I need to cluster them into 5 clusters but I couldn't figure this out in matlab.

Answers (5)

Walter Roberson
Walter Roberson on 26 Nov 2011
fcm(YourData, 5)
Each row of YourData should be a single sample.
  17 Comments
Aiman Almazroey
Aiman Almazroey on 28 Nov 2011
We know from FCM that it gives us the relation degree of each piece of data across all number of clusters.
therefore, we used the following command
[center,U,obj_fcn] = fcm(samples', 5,100);
now we need to plot them to see the 5 clusters but because we have more than 3 dimensions I don't know how to figure this out?
Aiman Almazroey
Aiman Almazroey on 29 Nov 2011
Is there any one can help in this plz?

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Aiman Almazroey
Aiman Almazroey on 26 Nov 2011
Please someone help on this

YASSER
YASSER on 27 Feb 2014
hi I have the same your's problem, have you solved it please
  2 Comments
Aiman Almazroey
Aiman Almazroey on 2 Mar 2014
The issue was solved. Please specify your problem in which part, so I can help you.
YASSER
YASSER on 6 Mar 2014
AFTER I apply FCM function, I don'knowt how to extract images clusters for example I use [center,U,obj_fcn] = fcm(data,3) what I have to do to get the 3 groups of Image

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soumi ghosh
soumi ghosh on 9 Apr 2014
Hello I have the same issue regarding fcm clusterinf of muti dimensional data set, need some help. Thank You
  1 Comment
Tamilalagan Natarajan
Tamilalagan Natarajan on 5 Sep 2014
Hi, I am trying to use FCM clustering. The only issue I still have is to identify the cluster center. If you have already found a solution, please can you share it.

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nur shidah ahmad
nur shidah ahmad on 8 Dec 2016
Do you have any idea to auto clustering the data? Since, i don't want set the cluster number and i want it to auto cluster.
  1 Comment
Walter Roberson
Walter Roberson on 8 Dec 2016
Yes, I know exactly how to get the best possible results in that situation: set the number of clusters to the number of unique points. Every cluster will then contain exactly one point (and any duplicates of it), which will always give you the best possible fitting, with no fitting error at all.

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