1. Efficient Mining of Both Positive and Negative Association Rules Xindong Wu (*), Chengqi Zhang (+), and Shichao Zhang (+) (*) University of Vermont, USA (+) University of Technology Sydney, Australia xwu@cs.uvm.edu Presenter: Mike Tripp 1

2. Outline Association Analysis Exceptions Problems Rules Examples Pruning Strategy Frequent Items of Potential Interest Infrequent Items of Potential Interest Procedure AllItemsOfInterest Extracting Positive and Negative Rules CPIR PositiveAndNegativeAssociations Effectiveness and Efficiency Experimental Results Related Work Exam Questions 2

3. Exceptions of Rules Known as a deviational pattern to a well-known fact, and exhibits unexpectedness. Also known as surprising patterns. Example: While birds(x) => flies(x), exception: bird(x), penguin(x) => −flies(x) Interesting fact: A => B as a valid rule does not imply −B => −A is a valid rule. 3

4. Key Problems in Negative Association Rule Mining How to effectively search for interesting itemsets. How to effectively identify negative association rules of interest. 4

5. Association Analysis Generate all large itemsets: All itemsets that have a support greater than or equal to the user specified minimum support are generated. Generate all the rules that have a minimum confidence in the following naive way: For every large itemset X and any B C X, let A = X − B. If the rule A => B has the minimum confidence (or supp(X)/supp(A) ≥ mc ), then it is a valid rule. 5

6. Negation/Types of Rules The negation of an itemset A is indicated by −A. The support of −A, supp(−A) = 1 − supp(A). In particular, for an itemset i1−i2i3, its support is supp(i1−i2i3 ) = supp(i1i3) − supp (i1i2i3). Positive rule: A => B Negative rules: A => −B −A => B −A => −B 6

7. Negative Association Rules Still Difficult: exponential growth of infrequent itemsets TD={(A,B,D);(B,C,D);(B,D);(B,C,D,E);(A,B,D,F)} Such a simple database contains 49 infrequent item sets. 7

8. Define Negative Association Rules Two cases: If both A and B are frequent, A U B is infrequent, is A=>~B a valid rule? If A is frequent, B is infrequent, is A => ~B a valid rule? Maybe, but not of our interest. Heuristic Heuristic: Only if both A and B are frequent, will A => ~B be considered. 8

9. Negative Association Example Consider supp(c) = 0.6, supp(t) = 0.4, supp(t U c) = 0.05, mc = 0.52 Confidence of t => c is supp(t U c)/supp[t] = 0.05/0.4 = 0.125, which is < mc = 0.52 and, supp(t U c) = 0.05 is low. This indicates that t U c is an infrequent itemset and that t => c cannot be a valid rule. However... Supp(t U −c) = supp[t] – supp[t U c] = 0.4 – 0.05 = 0.35 which is high, and the confidence of t => −c is the ratio supp[t U −c]/supp[t] = 0.35/0.4 = 0.875, which is > mc Therefore, t => −c is a valid rule. 9

10. Identifying Interesting Itemsets Because of the exponential number of infrequent itemsets in a database, pruning is critical to efficient search for interesting itemsets. 10

11. Pruning Strategy We want to find interesting itemsets when pruning. Let’s define an interestingness function interest(X, Y) = |supp(X U Y) – supp(X)supp(Y)| and a threshold mi If interest(X, Y) ≥ mi, then the rule X => Y is of potential interest, and X U Y is referred to as potentially interesting itemset Using this approach, we can establish an effective pruning strategy for efficiently identifying all frequent itemsets of potential interest in a database. The pruning strategy ensures we can use an Apriori like algorithm. Generating infrequent k itemsets from frequent k-1 itemsets. 11

12. Frequent Itemset of Potential Interest Where f() is a constraint function concerning the support, confidence, and interestingness of X => Y 12

13. Infrequent Itemset of Potential Interest Where g() is a constraint function concerning f() and the support, confidence, and interestingness of X => Y 13

14. Bringing them together Using the fipi and iipi mechanisms for both positive and negative rule discovery, our search is constrained to seeking interesting rules on certain measures, and pruning is the removal of all uninteresting branches that cannot lead to an interesting rule that would satisfy those constraints. 14

15. Procedure AllItemsOfInterest Input: D (a database); minsupp; mininterest Output: PL (frequent itemsets); NL (infrequent itemsets) 15

16. Procedure AllItemsOfInterest 16

17. Procedure AllItemsOfInterest E.g. run of the algorithm (ms=0.3,mi=0.05) 17 TIDItems bought T1{A,B,D} T2{A,B,C,D} T3 {B,D} T4 {B,C,D,E} T5 {A,E} T6 {B,D,F} T7 {A,E,F} T8 {C,F} T9 {B,C,F} T10 {A,B,C,D,F}

18. Procedure AllItemsOfInterest Generate frequent and infrequent 2-itemset of interest. When ms = 0.3, L2 = {AB, AD, BC, BD, BF, CD, CF}, N2 = {AC, AE, AF, BE, CE, DE, DF, EF} Use interest measure to prune. 18

19. Procedure AllItemsOfInterest So AD and CD are not of interest, they are removed from L2. 19

20. Procedure AllItemsOfInterest So the resulting frequent 2-itemsets are as follows: 20

21. Procedure AllItemsOfInterest Generate infrequent 2-itemsets useing the iipi measure. Very similar to frequent 2-itemsets. 21

22. Extracting Positive and Negative Rules Continue like this to get all the itemsets. 22 TIDItems bought T1{A,B,D} T2{A,B,C,D} T3 {B,D} T4 {B,C,D,E} T5 {A,E} T6 {B,D,F} T7 {A,E,F} T8 {C,F} T9 {B,C,F} T10 {A,B,C,D,F} Algorithm iteration Frequent 1-itemsetA,B,C,D,E,F Frequent 2-itemsetAB,BC,BD,BF,CF Infrequent 2-itemsetAC,AE,AF,BE, CE,CF,DE,EF Frequent 3-itemsetBCD Infrequent 3-itemset BCF,BDF

23. Extracting Positive and Negative Rules Pruning strategy for rule generation: Piatetsky-Shapiro’s argument. If Dependence(X,Y) = 1, X and Y are independent. If Dependence(X,Y) > 1, Y is positively dependent on X. The bigger the ratio (p(Y | X) – p(Y))/(1 – p(Y)), the higher the positive dependence. If Dependence(X,Y) < 1, Y is negatively dependent on X (~Y is positively dependent on X). The bigger the ratio (p(Y | X) – p(Y))/(−p(Y)), the higher the negative dependence. 23

24. Extracting Both Types of Rules Conditional probability increment ratio. Used to measure the correlation between X and Y. When CPIR(X|Y)=0, X and Y are dependent. When it is 1, they are perfectly correlated. When it is -1, they are perfectly negatively correlated. 24

25. Extracting Both Types of Rules Because p(~A)=1-p(A), we only need the first half of the previous equation. or This value is used as confidence value. 25

26. Association Rules of Interest Let I be the set of items in a database D, i = A U B ⊆ I be an itemset, A ∩ B = Ø, supp(A) ≠ 0, supp(B) ≠ 0, and ms, mc and mi > 0 be given by the user. Then, If supp(A U B) ≥ ms, interest(A, B) ≥ mi, and CIPR(B|A) ≥ mc, then A => B is a positive rule of interest If supp(A U −B) ≥ ms, supp(A) ≥ ms, supp(B) ≥ ms, interest(A, −B) ≥mi, and CPIR(−B|A) ≥ mc, then A => −B is a negative rule of interest If supp(−A U B) ≥ ms, supp(A) ≥ ms, supp(B) ≥ ms, interest(−A, B) ≥ mi, and CPIR(B|−A) ≥ mc, then −A => B is a negative rule of interest If supp(−A U −B) ≥ ms, supp(A) ≥ ms, supp(B) ≥ ms, interest(−A, −B) ≥ mi, and CPIR(−B|−A) ≥ mc, then −A => −B is a negative rule of interest 26

27. Example with CPIR For itemset B U D in PL B => D can be a valid positive rule of interest 27

28. Extracting rules One snapshot of an iteration in the algorithm The result B => −E is a valid rule. 28

29. Algorithm Design Generate the set PL of frequent itemset and the set NL of infrequent itemsets Extract positive rules of the form A => B in PL, and negative rules of the forms A => −B, −A => B, −A => −B in NL 29

30. PositiveAndNegativeAssociations 30

31. PositiveAndNegativeAssociations 31

32. Effectiveness and Efficiency Aggregated Test Data: used for KDD Cup 2000 Data and Questions Can be found: http://www.ecn.purdue.edu/KDDCUP/http://www.ecn.purdue.edu/KDDCUP/ Implemented on: Dell Workstation PWS650 w/ 2G of CPU and 2G memory Language: C++ 32

33. Experimental Results (1) A comparison with Apriori like algorithm without pruning MBP = Mining By Pruning MNP = Mining with No-Pruning 33

34. Experimental Results (2) A comparison with no-pruning 34

35. Experimental Results Effectiveness of pruning 35 PII = Positive Items of Interest NII = Negative Items of Interest

36. Related Work Negative relationships between frequent itemsets, but not how to find negative rules (Brin, Motwani and Silverstein 1997) Strong negative association mining using domain knowledge (Savasere, Ommiecinski and Navathe 1998) 36

37. Conclusions Negative rules are useful Pruning is essential to find frequent and infrequent itemsets. Pruning is important to find negative association rules. There could be more negative association rules if you have different conditions. 37

38. Exam Questions What are the three types dependencies when dependence(X, Y) are equal, greater than, and less than 1? If Dependence(X,Y) = 1, X and Y are independent. If Dependence(X,Y) > 1, Y is positively dependent on X. If Dependence(X,Y) < 1, Y is negatively dependent on X (−Y is positively dependent on X). 38

39. Exam Questions Give an example of a rule exception, or surprising pattern. While birds(x) => flies(x), exception: bird(x), penguin(x) => −flies(x) 39

40. Exam Questions What does CPIR(X|Y) tell us? When CPIR(X|Y)=0, X and Y are dependent. When it is 1, they are perfectly correlated. When it is -1, they are perfectly negatively correlated. 40