Data mining application with improved data mining algorithm selection

• US 20020138492A1
• Filed: 11/16/2001
• Published: 09/26/2002
• Est. Priority Date: 03/07/2001
• Status: Abandoned Application

• Current Assignee: Loyola Marymount University
• Original Assignee: Loyola Marymount University
• Inventors: Kil, David
• Application Number:
  US09/992,435
• Publication Number:
  US 20020138492A1
• Time in Patent Office: Days
• US Class Current: 707/100
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 18/40 Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A training database (including data mining algorithm descriptions and metafeatures characterizing probability density functions of features) in the memory and computer readable program code (i) to extract features that classify data, (ii) to calculate metafeatures describing the case probability density function, and (iii) to select a data mining algorithm by using the training database to map the calculated metafeatures describing the case probability density function to the selected data mining algorithm. The frequency of the occurrence of features with respect to datum in the data defining a case probability density function.

Claims

1. A data mining algorithm selection method for selecting a data mining algorithm for data mining analysis of a problem set, the data mining algorithm selection method comprising:

providing data to be analyzed by data mining;

providing a training database comprising a list of data mining algorithm instances, each data mining algorithm instance comprising a data mining algorithm description and a set of training metafeatures characterizing probability density functions of features;

extracting features that classify the data, the frequency of the occurrence of features with respect to datum in the data defining a case probability density function;

calculating metafeatures describing the case probability density function; and

selecting a data mining algorithm by using the training database to map the calculated metafeatures describing the case probability density function to the selected data mining algorithm.

2. The data mining algorithm selection method according to claim 1 further comprising updating the training database to include the selected data mining algorithm and the calculated metafeatures as a new data mining algorithm instance.

3. The data mining algorithm selection method according to claim 1, in which the extracting features further comprises:

identifying a point of diminishing returns with respect to the number of features extracted; and

estimating features robustness.

4. The data mining algorithm selection method according to claim 3, in which estimating feature robustness further comprises partitioning problem set data into subsets.

5. The data mining algorithm selection method according to claim 4, in which partitioning problem set data further comprises at least one act selected from the group consisting of partitioning the data set temporally, partitioning the data set sequentially, and partitioning the data set randomly.

6. The data mining algorithm selection method according to claim 4, in which estimating feature robustness further comprises calculating entropy of each subset as a statistical measure of similarity.

7. The data mining algorithm selection method according to claim 1 further comprising:

identifying a parameter; and

using the identified parameter in the act of selecting a data mining algorithm.

8. The data mining algorithm selection method according to claim 7 in which the parameter comprises at least one member selected from the group consisting of user preferences, real-time deployment issues, available memory, training data size, and available throughput.

9. The data mining algorithm selection method according to claim 1 in which selecting a data mining algorithm further comprises using a simple classifier.

10. The data mining algorithm selection method according to claim 1 in which selecting a data mining algorithm further comprises the act of using a Bayesian network.

11. The data mining algorithm selection method according to claim 1, in which act of calculating metafeatures describing the probability density function calculates metafeatures selected from a set consisting of the number of distinct modes of the probability density function, the degree of normality of the probability density function, a boundary-function description, and the degree of non-linearity of the probability density function.

12. The data mining algorithm selection method according to claim 1 further comprising:

selecting a plurality of data mining algorithms by using the training database to map the metafeatures describing the probability density function to the selected plurality of data mining algorithms; and

fusing the selected plurality of data mining algorithms into a composite data mining algorithm.

14. The data mining product embedded in a computer readable medium according to claim 13, the computer readable program code further comprising computer readable program code to update the training database to include the selected data mining algorithm and the calculated metafeatures as a new data mining algorithm instance.

15. The data mining product embedded in a computer readable medium according to claim 13, wherein the computer readable program code to extract features further comprises:

computer readable program code to identify a point of diminishing returns in the number of features; and

computer readable program code to estimate feature robustness.

16. The data mining product embedded in a computer readable medium according to claim 15, wherein the computer readable program code to estimate feature robustness further comprises computer readable program code to partition the data into subsets.

17. The data mining product embedded in a computer readable medium according to claim 16, wherein the computer readable program code to partition data further comprises computer readable program code selected from the set consisting of computer readable program code to partition the data set temporally, computer readable program code to partition the data set sequentially, and computer readable program code to partition the data set randomly.

18. The data mining product embedded in a computer readable medium according to claim 16, wherein the computer readable program code to estimate feature robustness further comprises computer readable program code to calculate the entropy of each subset as a statistical measure of similarity.

19. The data mining product embedded in a computer readable medium according to claim 13, the computer readable program code further comprising:

computer readable program code to identify parameters; and

computer readable program code to use the identified parameters in the computer readable program code for selecting a data mining algorithm.

20. The data mining product embedded in a computer readable medium according to claim 19, wherein the parameters selected from a set consisting of user preferences, real-time deployment issues, available memory, the training data size, and available throughput.

21. The data mining product embedded in a computer readable medium according to claim 13 wherein the computer readable program code to select a data mining algorithm further comprises computer readable program code to execute a simple classifier system.

22. The data mining product embedded in a computer readable medium according to claim 13 wherein the computer readable program code to select a data mining algorithm further comprises computer readable program code to execute a Bayesian network.

23. The data mining product embedded in a computer readable medium according to claim 13, wherein the computer readable program code to calculate metafeatures describing the probability density function calculates metafeatures selected from a group consisting of the number of distinct modes of the probability density function, the degree of normality of the probability density function, and the degree of non-linearity of the probability density function.

24. The data mining product embedded in a computer readable medium according to claim 13, further comprising:

computer readable program code to select a plurality of data mining algorithms by using the training database to map the metafeatures describing the probability density function to the selected plurality of data mining algorithms; and

computer readable program code to fuse the selected plurality of data mining algorithms into a composite data mining algorithm.

26. The data mining system according to claim 25 further comprising computer readable program code to update the training database to include the selected data mining algorithm and the calculated metafeatures as a new data mining algorithm instance.

27. The data mining system according to claim 25 further comprising:

computer readable program code to identify a point of diminishing returns in the number of features; and

computer readable program code to estimate feature robustness.

28. The data mining system according to claim 27, wherein the computer readable program code to estimate feature robustness further comprises computer readable program code to partition the data into subsets.

29. The data mining system according to claim 28, wherein the computer readable program code to partition data further comprises computer readable program code selected from the set consisting of computer readable program code to partition the data set temporally, computer readable program code to partition the data set sequentially, and computer readable program code to partition the data set randomly.

30. The data mining system according to claim 28, wherein the computer readable program code to estimate feature robustness further comprises computer readable program code to calculate the entropy of each subset as a statistical measure of similarity.

31. The data mining system according to claim 25, wherein the computer readable program code in the computer program product further comprises:

computer readable program code to identify parameters; and

computer readable program code to use the identified parameters in the computer readable program code for selecting a data mining algorithm.

32. The data mining system according to claim 31, with the parameters selected from a set consisting of user preferences, real-time deployment issues, available memory, the training data size, and available throughput.

33. The data mining system according to claim 25 wherein the computer readable program code to select a data mining algorithm further comprises computer readable program code to execute a simple classifier system.

34. The data mining system according to claim 25 wherein the computer readable program code to select a data mining algorithm further comprises computer readable program code to execute a Bayesian network.

35. The data mining system according to claim 25, wherein the computer readable program code to calculate metafeatures describing the probability density function calculates metafeatures selected from a group consisting of the number of distinct modes of the probability density function, the degree of normality of the probability density function, and the degree of non-linearity of the probability density function.

36. The data mining system according to claim 25, further comprising:

computer readable program code to select a plurality of data mining algorithms by using the training database to map the metafeatures describing the probability density function to the selected plurality of data mining algorithms; and

computer readable program code to fuse the selected plurality of data mining algorithms into a composite data mining algorithm.

38. The data mining system according to claim 37 further comprising computer readable program code to update the training database to include the selected data mining algorithm and the calculated metafeatures as a new data mining algorithm instance.

39. The data mining system according to claim 37 further comprising:

computer readable program code to identify a point of diminishing returns in the number of features; and

computer readable program code to estimate feature robustness.

40. The data mining system according to claim 39, wherein the computer readable program code to estimate feature robustness further comprises computer readable program code to partition the data into subsets.

41. The data mining system according to claim 40, wherein the computer readable program code to partition data further comprises computer readable program code selected from the set consisting of computer readable program code to partition the data set temporally, computer readable program code to partition the data set sequentially, and computer readable program code to partition the data set randomly.

42. The data mining system according to claim 40, wherein the computer readable program code to estimate feature robustness further comprises computer readable program code to calculate the entropy of each subset as a statistical measure of similarity.

43. The data mining system according to claim 37, wherein computer readable program code further comprises:

computer readable program code to identify parameters; and

computer readable program code to use the identified parameters in the computer readable program code for selecting a data mining algorithm.

44. The data mining system according to claim 43, wherein the identified parameters are selected from a set consisting of user preferences, real-time deployment issues, available memory, training data size, and available throughput.

45. The data mining system according to claim 37 wherein the computer readable program code to select a data mining algorithm further comprises computer readable program code to execute a simple classifier system.

46. The data mining system according to claim 37 wherein the computer readable program code to select a data mining algorithm further comprises computer readable program code to execute a Bayesian network.

47. The data mining system according to claim 37, wherein the computer readable program code to calculate metafeatures describing the probability density function calculates metafeatures selected from a set consisting of the number of distinct modes of the probability density function, the degree of normality of the probability density function, and the degree of non-linearity of the probability density function.

48. The data mining system according to claim 37, comprising:

computer readable program code to select a plurality of data mining algorithms by using the training database to map the metafeatures describing the probability density function to the selected plurality of data mining algorithms; and

computer readable program code to fuse the selected plurality of data mining algorithms into a composite data mining algorithm.

50. The data mining application according to claim 1 further comprising a means for updating the training database means to include the selected data mining algorithm and the metafeatures as a new data mining algorithm instance.

51. The data mining application according to claim 1 in which the means for extracting features further comprises:

a means for identifying a point of diminishing returns in the number of features; and

a means for estimating the robustness of features;

52. The data mining application according to claim 51, wherein the means for estimating feature robustness further comprises a means for partitioning problem set data into subsets.

53. The data mining application according to claim 52 wherein the means for partitioning problem set data uses a process selected from the set consisting of partitioning the data set temporally, partitioning the data set sequentially, and partitioning the data set randomly.

54. The data mining application according to claim 52, wherein the means for estimating feature robustness uses entropy of each subset as a statistical measure of similarity.

55. The data mining application according to claim 1 further comprised:

a means for identifying parameters;

wherein the means for directly mapping the metafeatures describing the probability density function to a selected data mining algorithm using the training database also uses the identified parameters.

56. The data mining application according to claim 54 wherein the parameters are selected from a set consisting of user preferences, real-time deployment issues, available memory, the size of training data, and available throughput.

57. The data mining application according to claim 1, wherein the means for directly mapping the metafeatures describing the probability density function to a selected data mining algorithm using the training database further comprises a simple classifier.

58. The data mining application according to claim 1, wherein the means for directly mapping the metafeatures describing the probability density function to a selected data mining algorithm using the training database further comprises a Bayesian network.

59. The data mining application according to claim 1, wherein the means for computing metafeatures computes metafeatures selected from a set consisting of the number of distinct modes of the probability density function, the degree of normality of the probability density function, and the degree of non-linearity of the probability density function.

60. The data mining application according to claim 1 further comprising

means for directly mapping the metafeatures describing the probability density function to a plurality of selected data mining algorithms using the training database;

and

means for fusing the plurality of selected data mining algorithms into a composite data mining algorithm.

13. A data mining product embedded in a computer readable medium, comprising:

at least one computer readable medium having a training database embedded therein and having a computer readable program code embedded therein to select a data mining algorithm,

the training database comprising a list of data mining algorithm instances, each data mining algorithm instance comprising a data mining algorithm description and a set of metafeatures characterizing probability density functions of features;

the computer readable program code comprising;

computer readable program code to extract features that classify data, the frequency of the occurrence of features with respect to datum in the data defining a probability density function;

computer readable program code to calculate metafeatures describing the probability density function;

computer readable program code to select a data mining algorithm by using the training database to map the calculated metafeatures describing the probability density function to the selected data mining algorithm.

25. A data mining system with improved data mining algorithm selection for data mining analysis of data, the data mining system comprising:

a general purpose computer comprising a memory and a central processing unit;

a training database in the memory, the comprising a list of data mining algorithm instances, each data mining algorithm instance comprising a data mining algorithm description and a set of metafeatures characterizing probability density functions of features;

computer readable program code to extract features that classify data, the frequency of the occurrence of features with respect to datum in the data defining a case probability density function;

computer readable program code to calculate metafeatures describing the case probability density function; and

computer readable program code to select a data mining algorithm by using the training database to map the calculated metafeatures describing the case probability density function to the selected data mining algorithm.

37. A data mining system with improved data mining algorithm selection for data mining analysis of data, the data mining system comprising:

a distributed network of computers;

a training database on the network, the training database comprising a list of data mining algorithm instances, each data mining algorithm instance comprising a data mining algorithm description and a set of metafeatures characterizing probability density functions of features;

computer readable program code to extract features that classify data, the frequency of the occurrence of features with respect to datum in the data defining a case probability density function; and

computer readable program code to calculate metafeatures describing the case probability density function;

computer readable program code to select a data mining algorithm by using the training database to map the calculated metafeatures describing the case probability density function to the selected data mining algorithm.

49. A data mining application with improved data mining algorithm selection for data mining analysis of a problem set, the data mining application comprising:

a training database means for storing a list of data mining algorithm instances, each data mining algorithm instance comprising a data mining algorithm description and a set of metafeatures characterizing probability density function of features over a problem data set;

a means for extracting features that classify problem set data, wherein the frequency of the occurrence of features with respect to datum in the problem data set defines a probability density function;

a means for computing metafeatures describing the probability density function; and

a means for directly mapping the metafeatures describing the probability density function to a selected data mining algorithm using the training database means.