# Making a group of maximum nearest elements.

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BHAGYALAKSHMI M on 11 Mar 2020
Commented: Guillaume on 17 Mar 2020
If I have some points and a data. I need to find the all the nearest elements of points. How can I do that ? Please help me.
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BHAGYALAKSHMI M on 12 Mar 2020
Edited: BHAGYALAKSHMI M on 12 Mar 2020
The nearest elements that are close to each point. I atttached a data here.
Suppose I have some elements A= [1,4,2,6,9,0,4.2,5.6,....] and points
B=[0.1,4.1,3.......]. Calculating and comparing the distance of these and based on that grouping.
Taking point 1 from A checking is this element is close to 0.1 or 4.1. It is close to 1 more than 4 so
G1=[1,0.1].
Taking point 4. It is close to 4.1, so
G2=[4,4.1].
2 is close to 3, so
G3=[2,3].
6 is close to 4.1 compared to others, so
G2=[4,4.1].
The process repeats until no elements in A.

Guillaume on 12 Mar 2020
Be aware that the following creates a temporary matrix of size numel(A) x numel(B), so if both vectors are very large you may run out of memory:
%for row vectors:
%example data
A = [1,4,2,6,9,0,4.2,5.6];
B = [0.1,4.1,3];
assert(isrow(A) && isrow(B), 'Inputs must be row vectors');
[~, groupA] = min(abs(A - B.'), [], 1)
%for column vectors:
%demo data
A = A.'; B = B.';
assert(iscolumn(A) && iscolumn(B), 'Inputs must be column vectors')
[~, groupA] = min(abs(A.' - B), [], 1)
I'm not sure how this should be applied to your example datasheet. Looking at what it contains, you should import that excel file in a table, in which case you should store the group as another variable of the table so you can then use aggregation functions such as groupsummary.
Guillaume on 17 Mar 2020
As I've said, this is typically a bad idea. I was asking about what you want to do after that. It is very likely that whatever it is, it will be much easier if you don't do this.
Now if you really insist:
group = accumarray(a(:), groupnumber(:), [], @(v) {v});
or
group = splitapply(@(v) {v}, a(:), groupnumber(:));

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