CPU and Memory Friendly Solution to Merge Large Matrix - matlab

Question

For the following typical case:
n = 1000000;
r = randi(n,n,2);
(assume there are 0.05% common numbers between all rows; n could be even tens of millions)
I am looking for a CPU and Memory efficient solution to merge rows based on any common items (here integer numbers). A list of sample codes in Python is available here and a quick try to translate one into Matlab can be found here.
In my attempt they take ages (minutes to hours), so I am in favor of finding faster solution.
For the above example, the typical output should look like (cell):
{
[1 90 34 67 ... 9]
[35 89]
[45000 23 828 130 8999 45326 ... 11]
...
}
Note also that, I have tried to compile as mex but failed due to no-support for cell in Matlab-Coder.
Edit: A tiny demonstration example
%---------------------------------------
clc
n = 100;
r = randi(n,n,2); % random integers in [1,n], size(n,2)
%---------------------------------------
>> r
r =
82 17 % (1) 82 17
91 13 % (2) 91 13
13 32 % (3) 91 13 32 merged with (2), common 13
82 53 % (4) 82 17 53 merged with (1), common 82
64 17 % (5) 82 17 53 64 merged with (4), common 17
...
94 45
13 31 % (77) 91 13 32 31 merged with (3), common 13
57 51
47 52
2 13 % (80) 91 13 32 31 2 merged with (77), common 13
34 80
%---------------------------------------
c = merge(r); % cpu and memory friendly solution is searched for.
%---------------------------------------
c =
[82 17 53 64]
[91 13 32 31 2]
...

Answer 1

You need an index.
In Python, use a dict. In MATLAB - I'd not use MATLAB, because open-source is the future, and MATLAB is dying out.
But Python is quite slow. You can likely get a 10x speedup by using e.g. Cython to translate and optimize the code in C. Avoid using Python data types such as a list of int, because they are very memory intensive. numpy has memory-efficient arrays of integer.
If you get a new pair (a,b) you can use this dictionary to find existing items to merge. Then update the dict after the merge.
Actually for integers, you should use an array instead of a dict.
The trickiest part is handling the case when both a and b exist, but are large different groups. There are some neat optimizations possible here, if that isn't fast enough yet.
It's not clustering, but connected components.