1. Action Association Rules Mining
Project proposal for Fall 2016
Jian Chen & Jennifer Lavergne

2. Project Goals To modify the itemset tree algorithm:
Discovering action sets
Generating action rules

3. Background

4. Application: Customer Attrition
Facts:
On average, most US corporations lose half of their customers
every five years
Longer a customer stays with the organization, the more
profitable he or she becomes
The cost of attracting new customers is five to ten times
more than retaining existing ones
About 14% to 17% of the accounts are closed for reasons
that can be controlled like price or service
Action:
Reducing the outflow of the customers by 5% can double
a typical company’s profit

5. Association Rules
Association rule - an implication {X ⇒ Y, support, confidence}. Where X and Y are subsets of the itemset I and X∩Y = Ø
Example: {{bread, milk} ⇒ {cheese}, 30%, 75%}
Support = #occurrences of I in database/#rows in database
Minsup – The minimum support threshold for an itemset I to be considered frequent
Confidence = Support(X ⋃ Y)/Support(X) for itemset I = X ⋃ Y.
Minconf – a user specified threshold that indicates the interestingness of a candidate rule I: conf(I) > minconf

6. Itemset Trees
A data structure which aids in users querying for a specific itemset and it’s support: Targeted Association Mining
Item mapped to numeric values: {bread} = {1}, {cheese} = {2}
Numbers must be in ascending order within the itemset
Ex: I = {1, 2, 56, 120}
Note: Can be used to find all or specific
rules within a dataset.

7. Rule generation

8. Rule Generation
minsup = 10% and minconf = 30% Rules generated using this method: {query} ⇒ {I - query} {2, 4} ⇒ {1}, support = 1/7 = 14%, confidence = 1/3 = 33% {2, 4} ⇒ {8}, support = 2/7 = 29%, confidence = 2/3 = 66%

9. Action association rules

10. Action Association Rules
Action Rule - Association rules with flexible and stable attributes.
Flexible attribute – objects which can potentially change from one state to another: (interest rate, low → high)
Stable attribute – objects which will remain stable and not change: (date of birth)
Action rule example:
(a, a1), (b, b1 → b2) ⇒ (d, d2 → d1)(c, c1)

11. Action Set Discovery A B C D x1 a1 b1 c1 d1 x2 a2 x3 b2 d2 x4 x5 x6 x7
(category, item) – support count
(A, a1) - support 2
(A, a2) - support 6
(B, b1) - support 4
(B, b2) - support 4
(C, c1) - support 6
(C, c2) - support 2
(D, d1) - support 4
(D, d2) - support 4
B and D are flexible
A and C are stable
MinSup = 3

12. Action Set Discovery A B C D x1 a1 b1 c1 d1 x2 a2 x3 b2 d2 x4 x5 x6 x7
(category, item change) – support count
(B, b1 → b2) - support 4
(B, b2 → b1) - support 4
(D, d1 → d2) - support 4
(D, d2 → d1) - support 4
The minimum of the two supports is kept and compared against MinSup.
Support(b1 → b2) = min(support(b1), support(b2))
B and D are flexible
A and C are stable
MinSup = 3

13. Action Set Discovery A B C D x1 a1 b1 c1 d1 x2 a2 x3 b2 d2 x4 x5 x6 x7
(A, a2) ・ (B, b1) - support 3
(A, a2) ・ (B, b2) - support 3
(A, a2) ・ (C, c1) - support 4
(A, a2) ・ (D, d1) - support 2
(A, a2) ・ (D, d2) - support 4
(B, b1) ・ (C, c1) - support 3
(B, b1) ・ (D, d1) - support 3
(B, b1) ・ (D, d2) - support 1
(B, b2) ・ (C, c1) - support 2
(B, b2) ・ (D, d1) - support 1
(B, b2) ・ (D, d2) - support 3
(C, c1) ・ (D, d1) - support 3
(C, c1) ・ (D, d2) - support 2
B and D are flexible
A and C are stable
MinSup = 3

14. Action Set Discovery A B C D x1 a1 b1 c1 d1 x2 a2 x3 b2 d2 x4 x5 x6 x7
(A, a2) ・ (B, b1 → b2) - support 3
(A, a2) ・ (B, b2 → b1) - support 3
(A, a2) ・ (D, d2 → d1) - support 2
(A, a2) ・ (D, d1 → d2) - support 2
(B, b1 → b2)・(D, d1) - support 1
(B, b1 → b2)・(D, d2) - support 1
(B, b1 → b2)・(D, d2 → d1) - support 1
(B, b1 → b2)・ (D, d1 → d2) - support 3
(C, c2)・(D, d2 → d1) - support 1
(C, c2)・(D, d1 → d2) - support 1
And so on….
B and D are flexible
A and C are stable
MinSup = 3

15. Action Rule Generation
Selection of rules generated from this dataset using MinSup and MinConf thresholds:
(B, b1 → b2)・ (D, d1 → d2) - support 3
(B, b1 → b2) ⇒ (D, d1 → d2)
(D, d1 → d2) ⇒ (B, b1 → b2)
There has to be a transition on each side of the association rule.

16. Project Description

17. Project description Modify the ordered itemset tree algorithm:
Discovering action sets
Generating action rules
Modify existing code or write your own.
Needs to be “attribute aware.”
Needs to keep track of flexible/stable
Read papers on itemset tree and action association rules (provided)

18. Project description
Need to map datasets to integer values without losing track of flexible and stable.
Modifications to search:
Additions to keep track of the flexible/stable attributes during search
Ability to search for itemsets as well as transitions or both simultaneously
Mark nodes and use subtrees to find transitions

19. Project description Modifications to rule generation:
Produce rules with both stable and flexible attributes
Has to have a transition on each side of the rule
(a, a2) (d, d1 → d2) ⇒ (b, b1 → b2)
(d, d1 → d2) ⇒ (a, a2) (b, b1 → b2)
(b, b1 → b2)⇒(d, d1 → d2)
All flexible attributes don’t have to have a transition

20. References
M. Kubat, A. Hafez, V. V. Raghavan, J. Lekkala, and W. K. Chen, “Itemset trees for targeted association mining”, IEEE Trans. on Knowledge and Data Engineering, 2002
Z.W. Ras, A. Dardzinska, L.-S. Tsay, H. Wasyluk, “Association Action Rules”, IEEE/ICDM Workshop on Mining Complex Data (MCD 2008), Pisa, Italy, ICDM Workshops Proceedings, IEEE Computer Society, 2008,
S. Im, Z.W. Ras, "Action rule extraction from a decision table: ARED“, in "Foundations of Intelligent Systems", Proceedings of ISMIS'08, A. An et al. (Eds.), Toronto, Canada, LNAI, Vol. 4994, Springer, 2008,
Z.W. Ras, A. Dardzinska, "Action Rules Discovery without pre-existing classification rules“ in Proceedings of RSCTC 2008 Conference, in Akron, Ohio, LNAI 5306, Springer, 2008,
D. Difallah, R. Benton, T. Johnsten and V. Raghavan, "FAARM: Frequent Association Action Rules Mining Using FP-Tree", in Workshop on Domain Driven Data Mining, part of 11th IEEE International Conference on Data Mining Workshops, Vancouver, Canada, pp , December 11, 2011.

21. Questions? Jian Chen, Ph.D. jchen@louisiana.edu
Jennifer Lavergne, Ph.D.