1. 2006/12/06Chen Yi-Chun1 Mining Positive and Negative Association Rules: An Approach for Confined Rules Maria-Luiza Antonie, Osmar R. Zaiane PKDD2004

2. 2006/12/06Chen Yi-Chun2 Outline Motivation –Negative association rule –Correlation coefficient Our algorithm Others’ algorithms Compare Conclusion

3. 2006/12/06Chen Yi-Chun3 Positive vs Negative Positive association rules –Typical association rule Negative association rules –Identify products that conflict with each other “customers that buy Coke do not buy Pepsi” –Identify products that complement each other “ 如果 Coke 賣完, 就改買 Pepsi”

4. 2006/12/06Chen Yi-Chun4 Example “non-organic organic” has 20% support and 25% confidence “ organic non-organic” has 20% support and 50% confidence Organic Non-organic206080 non-organic200 4060100 25% confidence

5. 2006/12/06Chen Yi-Chun5 Correlation Coefficient Correlation coefficient: = 0 : X and Y are independent. =+1: X and Y are perfectly positive correlated. =-1: X and Y are perfectly negative correlated. Let X and Y be two binary variables. – correlation coefficient: –Transform: Y Y X X N

6. 2006/12/06Chen Yi-Chun6 Summary The correlation coefficient between these two items is -0.61 –They are negatively correlated. –So the rule “ organic non-organic” is misleading

7. 2006/12/06Chen Yi-Chun7 Negative Association Rules Generalized negative association rule is a rule containing a negation of an item –e.g. : Confined negative association rules

8. 2006/12/06Chen Yi-Chun8 Correlation Coefficient (cont.) Cohen discusses about the correlation coefficient’s strength – is large – is moderate – is small Then we introduce an automatic progressive thresholding process –This eliminates the need for manually adjusted thresholds.

9. 2006/12/06Chen Yi-Chun9 Our Algo. Automatic progressive thresholding process. 1.We start by setting our correlation threshold to 0.5 2.If no strong correlated rules are found the threshold slides progressively to 0.4, 0.3 3.Until some rules are found with moderate correlations

10. 2006/12/06Chen Yi-Chun10 Others’ algo. [WZZ02] –Set a parameter “mininterest” – if mininterest That is interest. [THC02] SRM( substitution rule mining) –Only discuss one negative rule – 利用 -square value 和 support value 找出 concrete items – 再利用 correlation coefficient 找出其 rule

11. 2006/12/06Chen Yi-Chun11 Example TIDItems 1A,C,D 2B,C 3C 4A,B,F 5A,C,D 6E 7B,F 8B,C,F 9A,B,E 10A,D TIDItemsEquivalent bit vector 1A, B,C,D, E, F(101100) 2 A,B,C, D, E, F(011000) 3 A, B,C, D, E, F(001000) 4A,B, C, D, E,F(110001) 5A, B,C,D, E, F(101100) 6 A, B, C, D,E, F(000010) 7 A,B, C, D, E,F(010001) 8 A,B,C, D, E,F(011001) 9A,B, C, D,E, F(110010) 10A, B, C,D, E, F(100100)

12. 2006/12/06Chen Yi-Chun12 min. sup. = 0.2 correlation coefficient = 0.5 min. interest = 0.07 TI D Items 1A,C,D 2B,C 3C 4A,B,F 5A,C,D 6E 7B,F 8B,C,F 9A,B,E 10A,D OurInt.SRM ACD BD CE DF ABC ABD BCD Our Int. SRM AD BF 2-Itemsets 3-Itemsets 1.A: 5 B:5 C:5 D:3 E:2 F:3 2. 找出 candidate : AB, AC, AD, BC, BF, CD 3. 把彼此的 correlation 算出來若 又 其 則 XY 為 positive rule 4. 例 : A A D303 D257 5510 1. 若 又其 2. 則 Negative rule 會產生 3. 或是若 則會產生 4. 例 : BB D033 D527 5510 The itemset DF has a minimum interest of - 0.09, but it has a correlation of only -0.42 因為在 SRM 中其 correlation coefficient 必定要大於 minimum value, 而我們的方法只要大於等 於 minimum value 就好, 而 CE 的 correlation 剛好是 0.5

13. 2006/12/06Chen Yi-Chun13 Experimental Results

14. 2006/12/06Chen Yi-Chun14 Conclusion Too many association rules are generated but not always useful on marketing.