clustering nearest 5 elements of a data

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sreelekshmi ms
sreelekshmi ms le 15 Fév 2020
Commenté : sreelekshmi ms le 23 Fév 2020
I have 36 points I need to find the nearest 5 elements of each point and cluster it in a group. Then there is a total of 36 clusters will get. The points are in the 'core' and I need to find the nearest elements from the 'data'. How can I do that? Please help me.
clc;
clear;
data=xlsread('Pimaxl.xlsx');
Si=size(data);
asc=sort(data);
num = numel(data);
diff=unique(asc);
core1=maxk(diff,12);
core2=mink(diff,12);
n = numel(diff(:));
midpoint = ceil(n/2);
core3 = diff(midpoint-5:midpoint+6);
cor=[core1 core2];
core=[cor core3];
  2 commentaires
Rik
Rik le 15 Fév 2020
core is 12x3, while data is 768x9. What is your definition of point and what is your definition of distance? Is every single element a point and is the distance the absolute difference?
Turlough Hughes
Turlough Hughes le 15 Fév 2020
Have a look at the answer I provided here
You could do the same as that but just replace min with mink. Also as pointed out in the comments a sqrt is not needed.

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Turlough Hughes
Turlough Hughes le 15 Fév 2020
You could do the following which is to find the 5 minimum absolute differences for each element in core.
[~,idx] = mink(abs(data(:)-core(:).'),5,1);
clusters = data(idx)
Here, the input array to mink is 6912 by 36 where each row corresponds to an element in data and each column corresponds to an element in core and contains every possible absolute difference. The mink function returns an index that you can then sub directly back into data to get your clusters. Clusters is 5 by 36 with each column corresponding to the 36 values in core.
  24 commentaires
Turlough Hughes
Turlough Hughes le 22 Fév 2020
I can't say unless you show me what your partitioned data looks like. Refer back to my previous comment.
sreelekshmi ms
sreelekshmi ms le 23 Fév 2020
It looks like:

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