Systems And Methods For Mining Transactional And Time Series Data

• US 20070239753A1
• Filed: 04/05/2007
• Published: 10/11/2007
• Est. Priority Date: 04/06/2006
• Status: Active Grant

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: Leonard, Michael
• Granted Patent: US 7,711,734 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

In accordance with the teachings described herein, systems and methods are provided for analyzing transactional data. A similarity analysis program may be used that receives time-series data relating to transactions of an organization and performs a similarity analysis of the time-series data to generate a similarity matrix. A data reduction program may be used that receives the time-series data and performs one or more dimension reduction operations on the time-series data to generate reduced time-series data. A distance analysis program may be used that performs a distance analysis using the similarity matrix and the reduced time-series data to generate a distance matrix. A data analysis program may be used that performs a data analysis operation, such as a data mining operation, using the distance matrix to generate a data mining analysis of the transactional data.

Claims

1. A computer implemented method for generating a data mining analysis of transactional data, comprising:

receiving time-series data relating to transactions of an organization;

performing a similarity analysis of the time-series data to generate a similarity matrix;

performing one or more dimension reduction operations on the time-series data to generate reduced time-series data;

performing a distance analysis using the similarity matrix and the reduced time-series data to generate a distance matrix;

performing a data mining operation using the distance matrix to generate the data mining analysis of the transactional data; and

storing the data mining analysis in a computer-readable medium.

2. The method of claim 1, further comprising:

receiving time-stamped transactional data; and

accumulating the time-stamped transactional data to generate the time-series data.

3. The method of claim 1, wherein the one or more dimension reduction operations include a seasonal adjustment and decomposition analysis.

4. The method of claim 1, wherein the one or more dimension reduction operations include a component analysis.

5. The method of claim 1, wherein the one or more dimension reduction operations include a time domain analysis.

6. The method of claim 1, wherein the one or more dimension reduction operations include a frequency domain analysis.

7. The method of claim 1, wherein the one or more dimension reduction operations include fitting the time-series data to a time-series model.

8. The method of claim 1, wherein the data mining operation is a clustering analysis.

9. The method of claim 1, wherein the data mining operation is a sampling analysis.

10. The method of claim 1, wherein the data mining operation is a data classification.

11. The method of claim 1, wherein the data mining operation is a decision tree.

12. The method of claim 1, further comprising smoothing the time-series data prior to performing the similarity analysis.

13. The method of claim 1, further comprising combining the similarity matrix and the reduced time-series data into a data matrix, the distance analysis being performed using the data matrix.

14. The method of claim 13, wherein the data matrix is a series properties matrix.

15. The method of claim 1, wherein the similarity analysis is dynamic time warping analysis.

16. The method of claim 1, wherein the similarity analysis is a derivative dynamic time warping analysis.

17. The method of claim 1, wherein the similarity analysis is a least common subsequence similarity analysis.

18. The method of claim 1, further comprising accumulating the time-series data into a plurality of time-series vectors, wherein the similarity analysis and dimension reduction operations are performed using the time-series vectors.

19. A data analysis system for transactional data, comprising:

a similarity analysis program that receives time-series data relating to transactions and performs a similarity analysis of the time-series data to generate a similarity matrix;

a data reduction program that receives the time-series data and performs one or more dimension reduction operations on the time-series data to generate reduced time-series data;

a distance analysis program that performs a distance analysis using the similarity matrix and the reduced time-series data to generate a distance matrix; and

a data analysis program that performs a data analysis operation using the distance matrix to generate a data analysis of the transactional data.

20. The system of claim 19, further comprising:

a time-series accumulation program that receives time-stamped transactional data and accumulates the time-stamped transactional data to generate the time-series data.

21. The system of claim 19, wherein the one or more dimension reduction operations include a seasonal adjustment and decomposition analysis.

22. The system of claim 19, wherein the one or more dimension reduction operations include a component analysis.

23. The system of claim 19, wherein the one or more dimension reduction operations include a time domain analysis.

24. The system of claim 19, wherein the one or more dimension reduction operations include a frequency domain analysis.

25. The system of claim 19, wherein the one or more dimension reduction operations include fitting the time-series data to a time-series model.

26. The system of claim 19, wherein the data mining operation is a clustering analysis.

27. The system of claim 19, wherein the data mining operation is a sampling analysis.

28. The system of claim 19, wherein the data mining operation is a data classification.

29. The system of claim 19, wherein the data mining operation is a decision tree.

30. The system of claim 19, further comprising:

a data preparation program that smoothes the time-series data before it is received by the similarity analysis program.

31. The system of claim 19, further comprising:

a properties transformation program that combines the similarity matrix and the reduced time-series data into a data matrix, the distance analysis program using the data matrix to generate the distance matrix.

32. The system of claim 31, wherein the data matrix is a series properties matrix.

33. The system of claim 19, wherein the similarity analysis is dynamic time warping analysis.

34. The system of claim 19, wherein the similarity analysis is a derivative dynamic time warping analysis.

35. The system of claim 19, wherein the similarity analysis is a least common subsequence similarity analysis.

36. The system of claim 19, wherein the time-series data is accumulated into a plurality of time-series vectors, and wherein the similarity analysis and dimension reduction operations are performed using the time-series vectors.

37. The system of claim 19, wherein the data analysis program is a data mining program that performs a data mining operation.

38. A data mining system, comprising:

means for receiving a first set of time-stamped transactional data;

means for receiving a second set of time-stamped transactional data;

means for accumulating the first set of time-stamped transactional data to form a first set of time-series data;

means for accumulating the second set of time-stamped transactional data to form a second set of time-series data;

means for performing a similarity analysis of the first and second sets of time-series data to generate a similarity matrix;

means for performing a dimension reduction operation on the first and second sets of time-series data to generate a first reduced time-series and a second reduced time-series;

means for performing a distance analysis using the similarity matrix and the first and second reduced time-series to generate a distance matrix;

means for performing a data mining operation using the distance matrix to generate the data mining analysis of the transactional data; and

means for storing the data mining analysis in a computer-readable medium.