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.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
What is claimed is:
1. An inter-picture compression encoding apparatus, comprising:
inputting means for receiving decoded pictures;
picture type information generating means for generating a signal that represents a picture type corresponding to the decoded picture; and
GOP phase deviation determining means for determining the deviation of GOP phases corresponding to the output signal of said picture type information generating means.
2. The inter-picture compression encoding apparatus as set forth in claim 1,
wherein said picture type information generating means including:
information amount calculating means for calculating a value that represents an information amount assigned to each picture corresponding to the decoded pictures.
3. The inter-picture compression encoding apparatus as set forth in claim 2,
wherein said information amount calculating means calculates a value that represents an information amount of each pixel corresponding to a pixel value that is assigned to each pixel of a picture with reference to a predetermined threshold value, and
wherein said information amount calculating means adds the values that represent the information amounts of the pixels of the picture so as to calculate the value that represents the information amount assigned to each picture.
4. The inter-picture compression encoding apparatus as set forth in claim 1,
wherein the GOP phases are locked corresponding to the determined result of said GOP phase deviation determining means.
5. An inter-picture compression encoding apparatus, comprising:
inputting means for receiving decoded pictures;
encoding means for encoding the decoded pictures;
decoding means for decoding encoded pictures so as to generate re-decoded pictures;
picture quality deterioration evaluating means for calculating a value that represents the deterioration of the picture quality for each picture of a GOP corresponding to the decoded pictures and the re-decoded pictures; and
GOP phase deviation detecting means for detecting the deviation of GOP phases corresponding to a value that represents the deterioration of the picture quality.
6. The inter-picture compression encoding apparatus as set forth in claim 5,
wherein said GOP phase deviation detecting means detects the deviation of the GOP phases corresponding to a value that represents the deterioration of the picture quality of a plurality of pictures estimated as a predetermined number of successive B pictures corresponding to a GOP structure.
7. The inter-picture compression encoding apparatus as set forth in claim 5,
wherein said GOP phase deviation detecting means determines whether or not the value that represents the deterioration of the picture quality of a picture that is estimated as an I picture corresponding to a GOP structure is the maximum value of the GOP so as to determine the deviation of the GOP phases corresponding to the determined result.
8. The inter-picture compression encoding apparatus as set forth in claim 5,
wherein the value that represents the deterioration of the picture quality is signal noise ratio.
9. The inter-picture compression encoding apparatus as set forth in claim 5,
wherein the GOP phases are locked corresponding to the determined result of said GOP phase deviation detecting means.
10. An inter-picture compression encoding method, comprising the steps of:
receiving decoded pictures;
generating a signal that represents a picture type corresponding to the decoded pictures; and
determining the deviation of GOP phases corresponding to the output signal at the picture type information generating step.
11. An inter-picture compression encoding method, comprising the steps of:
receiving decoded pictures;
encoding the decoded pictures;
decoding encoded pictures so as to generate re-decoded pictures;
calculating a value that represents the deterioration of the picture quality for each picture of a GOP corresponding to the decoded pictures and the re-decoded pictures; and
detecting the deviation of GOP phases corresponding to a value that represents the deterioration of the picture quality.