clustering nearest 5 elements of a data

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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 Comments
Rik
Rik on 15 Feb 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 on 15 Feb 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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Accepted Answer

Turlough Hughes
Turlough Hughes on 15 Feb 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 Comments
Turlough Hughes
Turlough Hughes on 22 Feb 2020
I can't say unless you show me what your partitioned data looks like. Refer back to my previous comment.

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