APPARATUS AND METHOD FOR PREDICTING THE BEHAVIOR OR STATE OF A NEGATIVE OCCURRENCE CLASS

1 . A method for predicting the behavior or state of a negative occurrence class by scoring histories of members of the negative occurrence class against pasts of members of a positive occurrence class, the method comprising:

identifying the positive occurrence class, wherein each of the members of the positive occurrence class were previously members of the negative occurrence class;

determining positive occurrence class rules defining at least one cluster of the members of the positive occurrence class, wherein:

the positive occurrence class rules are based on the pasts of the members of the positive occurrence class; and

the pasts of the members of the positive occurrence class includes properties of the members of the positive occurrence class prior to becoming members of the positive occurrence class;

determining a center of each of the at least one cluster of the members of the positive occurrence class;

identifying the negative occurrence class, wherein each of the members of the negative occurrence class are currently members of the negative occurrence class;

determining negative occurrence class rules defining at least one cluster of the members of the negative occurrence class, wherein:

the negative occurrence class rules are based on the pasts of the members of the negative occurrence class; and

the histories of the members of the negative occurrence class includes properties of the members of the negative occurrence class;

determining a center of each of the at least one cluster of the members of the negative occurrence class;

determining a nearest cluster distance for each cluster of the members of the negative occurrence class, wherein the nearest cluster distance is the distance between the center of a given cluster of the members of the negative occurrence class to the center of the nearest cluster of the members of the positive occurrence class;

identifying the cluster of the members of the negative occurrence class having the smallest nearest cluster distance as the cluster of the members of the negative occurrence class that are most likely to next transition to members of the positive occurrence class.

2 . The method of claim 1 , wherein the pasts of the members of the positive occurrence class only includes properties of the members of the positive occurrence class prior to becoming a member of the positive occurrence class.

3 . The method of claim 1 , further comprising rank ordering the at least one clusters of the members of the negative occurrence class in order of the nearest cluster distance, wherein clusters having a smaller nearest cluster distance are more likely to next transition to the positive occurrence class.

4 . The method of claim 1 , wherein, in each of the at least one cluster of the members of the negative occurrence class, the members in a select cluster of the negative occurrence class are rank ordered as more likely to transition next to the positive occurrence class based on the distance of each of the members in the select cluster to the center of the select cluster and members in the select cluster closer to the center of the select cluster are more likely to next transition to the positive occurrence class.

5 . The method of claim 1 , wherein, in determining the nearest cluster distance, the nearest cluster of the members of the positive occurrence class is the cluster having a smallest weighted distance between the center of the given cluster of the members of the negative occurrence class to the center of the nearest cluster of the members of the positive occurrence class.

6 . The method of claim 5 , wherein a weighted distance is determined by weighting the distance between the center of a given cluster of the members of the negative occurrence class to the center of a given cluster of the members of the positive occurrence class by a weight applied to at least one of the given cluster of the members of the positive occurrence class or the given cluster of the members of the negative occurrence class.

7 . The method of claim 6 , wherein the weight applied to at least one of the given cluster of the members of the positive occurrence class or the given cluster of the members of the negative occurrence class is based on at least one of the number of members of or the density of the given cluster of the positive occurrence class and/or the given cluster of the negative occurrence class.

8 . The method of claim 1 , wherein the members of the negative occurrence class are users that are subscribers and members of the positive occurrence class are users that were previously subscribers that have unsubscribed.

9 . The method of claim 8 , wherein the members of the negative occurrence class are current subscribers to an electronic payment processing service.

10 . The method of claim 1 , wherein cluster analysis is used to determine the positive occurrence class rules and the negative occurrence class rules.

11 . The method of claim 10 , wherein determining the negative occurrence class rules and/or the positive occurrence class rules are performed using connectivity models, centroid models, distribution models, density models, subspace models, group models, or graph-based models.

12 . The method of claim 1 , wherein the pasts of the members of the positive class and the histories of the members of the negative class include at least one of time duration as a member, received member complaints, business size, or fees paid by the member.

13 . The method of claim 1 , further comprising identifying at least one remedial measure predicted to reduce the likelihood of members of the negative occurrence class having the smallest nearest cluster distance from transitioning to members of the positive occurrence class.

14 . An apparatus for predicting the behavior of a negative occurrence class by scoring histories of members of the negative occurrence class against pasts of members of a positive occurrence class, the apparatus comprising:

a database stored on a non-transitory computer readable medium, wherein the database includes data regarding the members of the positive occurrence class and data regarding the members of the negative occurrence class;

a processor configured to:

receive an identification of the positive occurrence class, wherein the members of the positive occurrence class were previously members of the negative occurrence class;

determine positive occurrence class rules defining at least one cluster of the members of the positive occurrence class, wherein:

the positive occurrence class rules are based on the pasts of the members of the positive occurrence class; and

the pasts of the members of the positive occurrence class includes properties of the members of the positive occurrence class prior to becoming a member of the positive occurrence class;

determine a center of each of the at least one cluster of the members of the positive occurrence class;

receive an identification of the negative occurrence class, wherein each of the members of the negative occurrence class are currently members of the negative occurrence class;

determine negative occurrence class rules defining at least one cluster of the members of the negative occurrence class, wherein:

the negative occurrence class rules are based on the pasts of the members of the negative occurrence class; and

the histories of the members of the negative occurrence class includes properties of the members of the negative occurrence class;

determine a center of each of the at least one clusters of the members of the negative occurrence class;

determine a nearest cluster distance for each cluster of the members of the negative occurrence class, wherein the nearest cluster distance is the distance between the center of a given cluster of the members of the negative occurrence class to the center of the nearest cluster of the members of the positive occurrence class;

identify the cluster of the members of the negative occurrence class having the smallest nearest cluster distance as the cluster of the members of the negative occurrence class that is most likely to next transition to the positive occurrence class.

15 . The apparatus of claim 14 , wherein the pasts of the members of the positive occurrence class stored in the database only includes properties of the members of the positive occurrence class prior to becoming a member of the positive occurrence class.

16 . The apparatus of claim 14 , wherein the processor is further configured to rank order the at least one clusters of the members of the negative occurrence class in order of the nearest cluster distance, wherein clusters having a smaller nearest cluster distance are more likely to next transition to the positive occurrence class.

17 . The apparatus of claim 14 , wherein, in determining the nearest cluster distance, the nearest cluster of the members of the positive occurrence class is the cluster having a smallest weighted distance between the center of the given cluster of the members of the negative occurrence class to the center of the nearest cluster of the members of the positive occurrence class.

18 . The apparatus of claim 17 , wherein a weighted distance is determined by weighting the distance between the center of a given cluster of the members of the negative occurrence class to the center of a given cluster of the members of the positive occurrence class by a weight applied to at least one of the given cluster of the members of the positive occurrence class or the given cluster of the members of the negative occurrence class.

19 . The apparatus of claim 18 , wherein the weight applied to at least one of the given cluster of the members of the positive occurrence class or the given cluster of the members of the negative occurrence class is based on at least one of the number of members of or the density of the given cluster of the positive occurrence class and/or the given cluster of the negative occurrence class.

20 . The apparatus of claim 14 , wherein:

in each of the at least one cluster of the members of the negative occurrence class, the processor is further configured to rank order the members in a select cluster of the negative occurrence class as more likely to transition next to the positive occurrence class based on the distance of each of the members in the select cluster to the center of the select cluster; and

members in the select cluster closer to the center of the select cluster are more likely to next transition to the positive occurrence class.

21 . The apparatus of claim 14 , wherein the members of the negative occurrence class are users that are subscribers and members of the positive occurrence class are users that were previously subscribers that have unsubscribed.

22 . The apparatus of claim 21 , wherein the members of the negative occurrence class are current subscribers to an electronic payment processing service.

23 . The apparatus of claim 14 , wherein the processor is further configured to perform cluster analysis to determine the positive occurrence class rules and the negative occurrence class rules.

24 . The apparatus of claim 23 , wherein the processor is further configured to determine the negative occurrence class rules and/or the positive occurrence class rules using connectivity models, centroid models, distribution models, density models, subspace models, group models, or graph-based models.

25 . The apparatus of claim 14 , wherein the pasts of the members of the positive class and the histories of the members of the negative class include at least one of time duration as a member, received member complaints, business size, or fees paid by the member.

26 . The apparatus of claim 14 , wherein the processor further configured to identify at least one remedial measure predicted to reduce the likelihood of members of the negative occurrence class having the smallest nearest cluster distance from transitioning to members of the positive occurrence class.