Applying data regression and pattern mining to predict future demand

• US 8,700,607 B2
• Filed: 07/27/2006
• Issued: 04/15/2014
• Est. Priority Date: 08/02/2005
• Status: Active Grant

• Current Assignee: Versata Development Group, Inc. (ESW Capital, LLC)
• Original Assignee: Versata Development Group, Inc. (ESW Capital, LLC)
• Inventors: Maag, Andrew
• Primary Examiner(s): Boccio, Vincent F
• Application Number:
  US11/460,401
• Publication Number:
  US 20070033185A1
• Time in Patent Office: 2,819 Days
• Field of Search: 707 1- 10, 707/719, 707/720, 707/751, 707/798
• US Class Current: 707/719
• CPC Class Codes: G06F 16/20 of structured data, e.g. relational data
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation

Abstract

A data processing system processes transaction database information to predict future demand using data regression techniques to extract trend line information from historical pattern frequency values. By extrapolating the trend line, a predicted pattern frequency value may be calculated. By applying regression techniques (such as least-squares approximation), the trend line information may be extracted and projected to predict the future pattern frequency which may be applied to calculate the expected value of a recommendation rule.

Claims

1. A computer-based method of mining one or more patterns from a transaction database, comprising:

identifying a first pattern in a transaction database that meets a minimum support threshold requirement;

for each of a plurality of predetermined time intervals, measuring a pattern frequency value for the first pattern based on the number of times the first pattern occurs in the predetermined time interval; and

processing the pattern frequency values for the first pattern to calculate a predicted pattern frequency value for the first pattern.

2. The method of claim 1, where the processing of pattern frequency values comprises applying a linear least-squares approximation to the pattern frequency values and extrapolating to the predicted pattern frequency value for the first pattern.

3. The method of claim 1, where the processing of pattern frequency values comprises computing a trend line based on the pattern frequency values and extrapolating the trend line to calculate the predicted frequency value for the first pattern.

4. The method of claim 1, where the processing of pattern frequency values comprises using a regression analysis to calculate the predicted frequency value for the first pattern.

5. The method of claim 1, further comprising capping the predicted pattern frequency value to an upper limit to prevent the predicted pattern frequency value from exceeding the upper limit.

6. The method of claim 1, further comprising capping the predicted pattern frequency value to an lower limit to prevent the predicted pattern frequency value from going below the lower limit.

7. The method of claim 1, further comprising using the predicted pattern frequency value to calculate an expected value of a recommendation rule that is based the first pattern.

8. The method of claim 1, where the plurality of predetermined time intervals comprises a plurality of recent time intervals.

9. The method of claim 1, where the plurality of predetermined time intervals comprises a plurality of constant time intervals.

10. An article of manufacture having at least one recordable medium having stored thereon executable instructions and data which, when executed by at least one processing device, cause the at least one processing device to:

identify a first pattern in a transaction database that meets a minimum support threshold requirement;

measure, for each of a plurality of predetermined time intervals, a pattern frequency value for the first pattern based on how many times the first pattern occurs in the predetermined time interval; and

process the pattern frequency values for the first pattern to calculate a predicted pattern frequency value for the first pattern.

11. The article of manufacture of claim 10, wherein the processing device processes the pattern frequency values by applying a linear least-squares approximation to the pattern frequency values and extrapolating to the predicted pattern frequency value for the first pattern.

12. The article of manufacture of claim 10, wherein the processing device processes the pattern frequency values by computing a trend line based on the pattern frequency values and extrapolating the trend line to calculate the predicted frequency value for the first pattern.

13. The article of manufacture of claim 10, wherein the processing device processes the pattern frequency values by using a regression analysis to calculate the predicted frequency value for the first pattern.

14. The article of manufacture of claim 10, wherein the executable instructions and data, when executed by at least one processing device, cause the at least one processing device to cap the predicted pattern frequency value to an upper limit to prevent the predicted pattern frequency value from exceeding the upper limit.

15. The article of manufacture of claim 10, wherein the executable instructions and data, when executed by at least one processing device, cause the at least one processing device to cap the predicted pattern frequency value to an lower limit to prevent the predicted pattern frequency value from going below the lower limit.

16. The article of manufacture of claim 10, wherein the executable instructions and data, when executed by at least one processing device, cause the at least one processing device to use the predicted pattern frequency value to calculate an expected value of a recommendation rule that is based the first pattern.

17. A system for mining pattern frequencies to calculate a predicted pattern frequency, comprising:

a database for storing a plurality of transactions, comprising a purchase item having a first pattern frequency; and

a processing engine to;

measure, for each of a plurality of predetermined time intervals, a pattern frequency value for the first pattern frequency based on how many times the first pattern frequency occurs in the predetermined time interval; and

process the pattern frequency values for the first pattern frequency to calculate a predicted pattern frequency for the first pattern frequency.

18. The system of claim 17, where the processing engine calculates the predicted pattern frequency by applying a linear least-squares approximation to the pattern frequency values.

19. The system of claim 17, where the processing engine calculates the predicted pattern frequency by computing a trend line based on the pattern frequency values and extrapolating the trend line to calculate the predicted pattern frequency.

20. The system of claim 17, where the processing engine calculates the predicted pattern frequency by using a regression analysis to calculate the predicted frequency value for the first pattern.