초록 ▼

A computer-implemented data mining system that analyzes customer transaction data using Factor Analysis/Retail Data Mining Segmentation. The data is accessed from a relational database, and then a factor analysis function is performed on the data to create a factor loadings matrix that has factors a

A computer-implemented data mining system that analyzes customer transaction data using Factor Analysis/Retail Data Mining Segmentation. The data is accessed from a relational database, and then a factor analysis function is performed on the data to create a factor loadings matrix that has factors as columns and observed variables from the customer transaction data as rows, wherein each of the observed variables is assigned to one of the factors in the factor loadings matrix that has the maximum value for the row. New variables are derived by means of a factor-scoring method that combines the variables into the factors in the factor loadings table. Customer destination segments are identified from the relational database using the factors. Additional customer destination segments are identified by means of a clustering tool using the derived new variables.

대표청구항 ▼

What is claimed is: 1. A method for analyzing data in a computer-implemented data mining system, comprising: (a) accessing customer transaction data from a relational database in the computer-implemented data-mining system; (b) performing a factor analysis function on the customer transaction data

What is claimed is: 1. A method for analyzing data in a computer-implemented data mining system, comprising: (a) accessing customer transaction data from a relational database in the computer-implemented data-mining system; (b) performing a factor analysis function on the customer transaction data in the computer-implemented data mining system to create a factor loadings matrix that has factors as columns and observed variables from the customer transaction data as rows, wherein each of the observed variables is assigned to one of the factors in the factor loadings matrix that has a maximum value for the row; (c) deriving new variables in the computer-implemented data mining system by means of a factor-scoring method that combines the new variables into the factors in the factor loadings matrix; and (d) identifying customer destination segments from the relational database in the computer-implemented data mining system using the factors and the new variables; (e) using the identified customer destination segments for analyzing data in the computer implemented data mining system. 2. The method of claim 1, wherein the customer transaction data is comprised of baskets. 3. The method of claim 2, wherein each of the factors in the factor loadings matrix represents an affinity group of the observed variables that account for a specified percentage of a baskets total dollar value. 4. The method of claim 3, wherein each of the affinity groups is used to define one or more customer destination segments from the customer transaction data. 5. The method of claim 1, wherein the factor-scoring method uses scores generated by a data reduction function. 6. The method of claim 1, wherein the factor-scoring method uses an unweighted sum of variables assigned to each factor. 7. The method of claim 1, wherein the factor-scoring method generates factor scores as the new variables. 8. The method of claim 1, wherein the identifying step comprises selecting a subset of baskets related to each of the factors. 9. The method of claim 8, further comprising generating a profile for the selected subset of baskets. 10. The method of claim 1, further comprising performing a clustering function using the new variables to search for the customer destination segments. 11. The method of claim 10, wherein the clustering function uses only a first one of the factors to derive the new variables for use by the clustering function. 12. The method of claim 1, further comprising identifying customer destination segments from the relational database in the computer-implemented data mining system by means of a clustering tool using the new variables. 13. A computer-implemented data mining system for analyzing data, comprising: (a) a computer; (b) logic, performed by the computer, for: (1) accessing customer transaction data from a relational database in the computer-implemented data mining system; (2) performing a factor analysis function on the customer transaction data in the computer-implemented data mining system to create a factor loadings matrix that has factors as columns and observed variables from the customer transaction data as rows, wherein each of the observed variables is assigned to one of the factors in the factor loadings matrix that has a maximum value for the row; (3) deriving new variables in the computer-implemented data mining system by means of a factor-scoring method that combines the new variables into the factors in the factor loadings matrix; and (4) identifying customer destination segments from the relational database in the computer-implemented data mining system using the factors and the new variables; (5) using the identified customer destination segments for analyzing data in the computer implemented data mining system. 14. The system of claim 13, wherein the customer transaction data is comprised of baskets. 15. The system of claim 14, wherein each of the factors in the factor loadings matrix represents an affinity group of the observed variables that account for a specified percentage of a baskets total dollar value. 16. The system of claim 15, wherein each of the affinity groups is used to define one or more customer destination segments from the customer transaction data. 17. The system of claim 13, wherein the factor-scoring method uses scores generated by a data reduction function. 18. The system of claim 13, wherein the factor-scoring method uses an unweighted sum of variables assigned to each factor. 19. The system of claim 13, wherein the factor-scoring method generates factor scores as the new variables. 20. The system of claim 13, wherein the logic for identifying comprises logic for selecting a subset of baskets related to each of the factors. 21. The system of claim 20, further comprising logic for generating a profile for the selected subset of baskets. 22. The system of claim 13, further comprising logic for performing a clustering function using the new variables to search for the customer destination segments. 23. The system of claim 22, wherein the clustering function uses only a first one of the factors to derive the new variables for use by the clustering function. 24. The system of claim 23, further comprising logic for identifying customer destination segments from the relational database in the computer-implemented data mining system by means of a clustering tool using the new variables. 25. An article of manufacture tangibly embodied on a computer readable medium embodying logic for analyzing data in a computer-implemented data mining system, the logic comprising: (a) accessing customer transaction data from a relational database in the computer-implemented data mining system; (b) performing a factor analysis function on the customer transaction data in the computer-implemented data mining system to create a factor loadings matrix that has factors as columns and observed variables from the customer transaction data as rows, wherein each of the observed variables is assigned to one of the factors in the factor loadings matrix that has a maximum value for the row; (c) deriving new variables in the computer-implemented data mining system by means of a factor-scoring method that combines the new variables into the factors in the factor loadings matrix; and (d) identifying customer destination segments from the relational database in the computer-implemented data mining system using the factors and the new variables; (e) using the identified customer destination segments for analyzing data in the computer implemented data mining system. 26. The article of manufacture of claim 25, wherein the customer transaction data is comprised of baskets. 27. The article of manufacture of claim 26, wherein each of the factors in the factor loadings mat represents an affinity group of the observed variables that account for a specified percentage of a basket's total dollar value. 28. The article of manufacture of claim 27, wherein each of the affinity groups is used to define one ox mote customer destination segments from the customer transaction data. 29. The article of manufacture of claim 25, wherein the factor-scoring method uses scores generated by a data reduction fraction. 30. The article of manufacture of claim 25, wherein the factor-scoring method uses an unweighted sum of variables assigned to each factor. 31. The article of manufacture of claim 25, wherein the factor-scoring method generates factor scores as the new variables. 32. The article of manufacture of claim 25, wherein the logic for identifying comprises logic for selecting a subset of baskets related to each of the factors. 33. The article of manufacture of claim 32, further comprising generating a profile for the selected subset of baskets. 34. The article of manufacture of claim 25, further comprising performing a clustering function using the new variables to search for the customer destination segments. 35. The article of manufacture of claim 34, wherein the clustering function uses only a first one of the factors to derive the new variables for use by the clustering function. 36. The article of manufacture of claim 35, further comprising identifying customer destination segments from the relational database in the computer-implemented data mining system by means of a clustering tool using the new variables.