Processing mixed numeric and/or non-numeric data

• US 20040019574A1
• Filed: 04/18/2003
• Published: 01/29/2004
• Est. Priority Date: 04/19/2002
• Status: Active Grant

• Current Assignee: Computer Associates Think Inc. (Broadcom, Inc.)
• Original Assignee: Computer Associates Think Inc. (Broadcom, Inc.)
• Inventors: Duan, Baofu, Pao, Yoh-Han, Meng, Zhuo, Cass, Ronald J.
• Granted Patent: US 7,716,148 B2
• Time in Patent Office: Days
• US Class Current: 706/15
• CPC Class Codes: G06F 16/2462 Approximate or statistical queries
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

An apparatus and method for processing mixed data for a selected task is provided. An input transformation module converts mixed data into converted data. A functional mapping module processes the converted data to provide a functional output for the selected task. The selected task may be one or a combination of a variety of possible tasks, including search, recall, prediction, classification, etc. For example, the selected task may be for data mining, database search, targeted marketing, computer virus detection, etc.

Claims

1. An apparatus for processing mixed data for a selected task, comprising:

an input transformation module adapted to transform the mixed data into converted data; and

a functional mapping module adapted to process the converted data to provide a functional output for the selected task.

2. The apparatus of claim 1, wherein the input transformation module uses a signpost transformation to transform the mixed data into converted data.

3. The apparatus of claim 2, wherein cluster centers are set as reference points and distances from a mixed data to the respective reference points correspond to dimensions of the converted data space.

4. The apparatus of claim 2, wherein the input transformation module is trained through clustering of a mixed data training set.

5. The apparatus of claim 4, wherein the input transformation module uses a supervised learning methodology.

6. The apparatus of claim 4, wherein the input transformation module uses a k-means methodology for determining cluster centers.

7. The apparatus of claim 4, wherein the input transformation module uses a k-medoids methodology for determining cluster centers.

8. The apparatus of claim 1, wherein the input transformation module uses an encoding methodology to transform the mixed data into converted data.

9. The apparatus of claim 1, wherein the mixed data includes consumer profile information.

10. The apparatus of claim 1, wherein the converted data is in a numerical representation.

11. The apparatus of claim 1, wherein the mixed data corresponds to text.

12. The apparatus of claim 1, wherein the input transformation module learns to organize mixed data patterns into sets corresponding to a plurality of nodes, and respective outputs of the nodes correspond to said converted data.

13. The apparatus of claim 12, wherein each node has an associated cluster annotation function.

14. The apparatus of claim 12, wherein the learning is unsupervised.

15. The apparatus of claim 1, wherein the functional mapping module includes a computational model with at least one basis function, and parameters of the at least one basis function are adjusted as the functional mapping module learns a training set of sample patterns associated with the selected task.

16. The apparatus of claim 15, wherein the functional mapping module includes a functional link net.

17. The apparatus of claim 15, wherein the functional mapping module includes an orthogonal functional link net.

18. The apparatus of claim 15, wherein the functional mapping module uses a regression technique for adjusting the parameters of the at least one basis function.

19. The apparatus of claim 18, wherein the at least one basis function includes a sigmoid.

20. The apparatus of claim 18, wherein the at least one basis function includes a wavelet.

21. The apparatus of claim 18, wherein the at least one basis function includes a radial basis function.

22. The apparatus of claim 18, wherein the at least one basis function includes a polynomial.

23. The apparatus of claim 15, wherein the learning by the functional mapping module is by a supervised, recursive least squares estimation method.

24. The apparatus of claim 15, wherein the functional mapping module includes a feed-forward net.

25. The apparatus of claim 24, wherein the feed-forward net is non-linear.

26. The apparatus of claim 24, wherein the feed-forward net learns by back-propagation of error.

27. The apparatus of claim 1, wherein the input transformation module and the functional mapping module comprise respective layers of a neural network.

28. The apparatus of claim 1, wherein the selected task is data mining.

29. The apparatus of claim 1, wherein the selected task is database search.

30. The apparatus of claim 1, wherein the selected task is targeted marketing.

31. The apparatus of claim 1, wherein the selected task is computer virus detection.

32. The apparatus of claim 1, wherein the selected task is one of visualization, search, recall, prediction and classification.

33. A method of processing mixed data for a selected task, comprising:

transforming mixed data into converted data; and

processing the converted data to provide a functional output for the selected task.

34. The method of claim 33, wherein the mixed data is transformed into converted data through a signpost transformation.

35. The method of claim 34, wherein cluster centers are set as reference points and distances from a mixed data to the respective reference points correspond to dimensions of the converted data space.

36. The method of claim 33, wherein the mixed data is transformed into converted data through an encoding methodology.

37. The method of claim 36, wherein the mixed data includes consumer profile information.

38. A computer data signal embodied in a transmission medium which embodies instructions executable by a computer to perform the method of claim 33.

39. A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform the method of claim 33.

40. A computing system, comprising:

a processor; and

a program storage device readable by the computer system, tangibly embodying a program of instructions executable by the processor to perform the method of claim 33.