Yes, you can apply the Jenks Natural Breaks iteratively to split the array into several classes based on the similarity of the elements. I wrote a function that applies this method to a one-dimensional array to split it into two classes. You can use it several times while updating the data array.
Function: https://www.mathworks.com/matlabcentral/fileexchange/72677-clustering-via-jenks-natural-breaks
Example:
data = [1,1,2,3,10,11,13,67,71];
total = length (data);
% Split the initial array into two classes based on Jenks Natural Breaks
[SDCM_All, GF] = get_jenks_interface(data);
% get the first interface: index of maximum Goodness of Variance Fit
[M, I1] = max(GF);
% extract sub_array 3
sub_array_3 = data(I1+1:total);
% get the reamining elements
remaining_elements = data (1:I1);
total = length(remaining_elements);
% Split the remaining elements into two classes based on Jenks natural breaks
[SDCM_All, GF] = get_jenks_interface(remaining_elements);
% get the second interface: index that has the maximum Goodness of Variance Fit
[M, I2] = max(GF);
% extract sub_array_2
sub_array_2 = data(I2+1:total);
% extract sub_array_1
sub_array_1 = data(1:I2);
disp(sub_array_1);
disp(sub_array_2);
disp(sub_array_3);
>>>>>>>> Output:
>> main
1 1 2 3
10 11 13
67 71
