I have different Features in my dataset these features names as following A B C D E F G H
There is a correlation between these features
Features Correlation
----------------------
A B 70
A C 78
B C 96
A G 93
.
.
.
Therefore, I would like to group similar features together so they can be represented by one feature
Something Like this
Seed Group Correlations Avg
-----------------------------------
A D & G 98 + 93 / 2 = 95.5
B F & C & E 85 + 96 + 79 / 3 = 86.6
..
..
..
H - -
So I get all close correlations in the same group
Another view to the problem
multiple cities in the country (City A B C D.. H)
Each city has a connection to another city
Cities Connection %
----------------------
A B 70
A C 78
B C 96
A G 93
.
.
.
We would like to hire area managers where cities with close connections can be served by the same area manager
We want to have the optimal number of area managers and where they should reside
Office Area Other Served Areas Connection Avg
------------------------------------------------------
A D & G 98 + 93 / 2 = 95.5
B F & C & E 85 + 96 + 79 / 3 = 86.6
..
..
..
H - -
I just want a method of how to figure how to split these features/cities in an optimum way that can cover most features/cities with a minimum number of links/area managers
