Intelligent clustering system

1. A computer-implemented method for forming a plurality of stores into a plurality of store clusters based on price optimization, and re-optimizing prices based on the plurality of store clusters, comprising:

collecting store specific information from a plurality of stores;

optimizing prices for a plurality of products for each individual store of the plurality of stores, and wherein the price optimization uses demand coefficients, cost coefficients and optimization rules;

creating a plurality of store clusters from the plurality of stores based on the closeness of the optimized prices of the plurality of products for each individual store, based on store specific information, and based on demand group structure of the plurality of products, and wherein the demand group structure of the plurality of products is based on substitutable products;

re-optimizing prices for the plurality of products for at least one of the plurality of store clusters, wherein the re-optimizing of prices uses demand coefficients, cost coefficients and optimization rules, and wherein the re-optimizing of prices is implemented on a computer; and providing the re-optimized prices to the at least one of the plurality of store clusters; and

wherein the closeness of the optimized prices of the plurality of products is computed using a distance equation:

Distance=√{square root over ((Price s1,x −Price s2,x) ) 2 +(Price s1,y −Price s2,y ) 2 )}{square root over ((Price s1,x −Price s2,x) ) 2 +(Price s1,y −Price s2,y ) 2 )}

wherein s1 and s2 are stores in the at least one of the plurality of store clusters, and wherein x and y are two of the optimized prices.

2. The method, as recited in claim 1 , further comprising providing cluster based combinations.

3. The method, as recited in claim 2 , wherein the store specific information is selected from a group comprising point-of-sales data, customer survey data, and cost data.

4. The method, as recited in claim 3 , wherein the combinations further include assortment and promotion combinations.

5. The method, as recited in claim 1 , wherein the creating the plurality of clusters, comprises:

providing at least one constraint; and

placing stores that meet the constraints and with the closest optimal combinations in the same cluster of the plurality of store clusters.

6. The method, as recited in claim 5 , wherein the at least one constraint places two stores in the same cluster, by making each store of the two stores have the same optimal combination.

7. The method, as recited in claim 5 , wherein the at least one constraint specifies a maximum number of clusters.

8. A computer-readable medium having computer-readable instructions embedded therein, which, when executed by a computer, causing said computer to implement a method for forming a plurality of stores into a plurality of store clusters based on price optimization, and re-optimizing prices based on the plurality of store clusters, comprising:

collecting store specific information from a plurality of stores;

optimizing prices for a plurality of products for each individual store of the plurality of stores, and wherein the price optimization uses demand coefficients, cost coefficients and optimization rules;

creating a plurality of store clusters from the plurality of stores based on the closeness of optimized prices of the plurality of products for each individual store, based on store specific information, and based on demand group structure of the plurality of products, and wherein the demand group structure of the plurality of products is based on substitutable products;

re-optimizing prices for the plurality of products for at least one of the plurality of store clusters, and wherein the re-optimizing of prices uses demand coefficients, cost coefficients and optimization rules;

providing the re-optimized prices to the at least one of the plurality of store clusters; and

wherein the closeness of the optimized prices of the plurality of products is computed using a distance equation:

Distance=√{square root over ((Price s1,x −Price s2,x) ) 2 +(Price s1,y −Price s2,y ) 2 )}{square root over ((Price s1,x −Price s2,x) ) 2 +(Price s1,y −Price s2,y ) 2 )}

wherein s1 and s2 are stores in the at least one of the plurality of store clusters, and wherein x and y are two of the optimized prices.

9. The computer-readable medium, as recited in claim 8 , further comprising computer-readable instructions for causing said computer to provide cluster based combinations.

10. The computer-readable medium, as recited in claim 9 , wherein the store specific information is selected from a group comprising point-of-sales data, customer survey data, and cost data.

11. The computer-readable medium, as recited in claim 10 , wherein the combinations further include assortment and promotion combinations.

12. The computer-readable medium, as recited in claim 8 , wherein creating the plurality of clusters, comprises:

providing at least one constraint; and

placing stores that meet the constraints and with the closest optimal combinations in the same cluster of the plurality of store clusters.

13. The computer-readable medium, as recited in claim 12 , wherein the at least one constraint places two stores in the same cluster, by making each store of the two stores have the same optimal combination.

14. The computer-readable medium, as recited in claim 12 , wherein the at least one constraint specifies a maximum number of clusters.

15. The method, as recited in claim 1 , further comprising providing at least one constraint and wherein the at least one constraint prohibits two stores of the plurality of stores from being in the same cluster.

16. The method, as recited in claim 5 , wherein the at least one constraint places two stores in the same cluster, by averaging the prices of an item and placing the average price as the price of the item in each store.

17. The method, as recited in claim 5 , wherein the at least one constraint places stores with a geographical closeness in the same cluster.

18. The computer-readable medium as recited in claim 8 , further comprises computer-readable instructions for causing said computer to provide at least one constraint and wherein the at least one constraint prohibits two stores of the plurality of stores from being in the same cluster.

19. The computer-readable medium as recited in claim 12 , wherein the at least one constraint places two stores in the same cluster, by averaging the prices of an item and placing the average price as the price of the item in each store.

20. The computer-readable medium as recited in claim 12 , wherein the at least one constraint places stores with a geographical closeness in the same cluster.