1. 1 Synthesizing High-Frequency Rules from Different Data Sources Xindong Wu and Shichao Zhang IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 15, NO. 2, MARCH/APRIL 2003

2. 2 Pre-work Knowledge management. Knowledge discovery Data mining. Data warehouse

3. 3 Knowledge Management Building data warehousing by Knowledge management

4. 4 Knowledge Discovery and Data Mining Data mining is a tool of knowledge discovery

5. 5 Why data mining Simon Commodities Supermarket If a supermarket manager, simon, want to arrange these commodities into supermarket, how to do will make more revenues, conveniences …. if one customer buys milk then he is likely to buy bread, so...

6. 6 Why data mining Simon Before long, if simon want to send some advertisement letters for customers, how to consider the individual differences is an important task. Mary always buys diapers and milk powders, she may have a baby, so ….

7. 7 The role of Data mining Preprocess data Useful patterns Knowledge and strategy

8. 8 Mining association rules Bread Milk IF bread is bought then milk is bought

9. 9 Mining steps step1: define minsup and minconf ex: minsup=50% minconf=50% step2: find large itemsets step3: generate association rules

10. 10 Example Large itemsets

11. 11 Outline Introduction Weights of Data Sources Rule Selection Synthesizing High-Frequency Rules Algorithm Relative Synthesizing Model Experiments Conclusion

12. 12 AB→C A→D B→E AB→C A→D B→E Introduction Framework DB 1 DB 2... DB n RD 1 RD 2 RD n... GRB Synthesizing High-Frequency Rules Weighting Ranking AB→C A→D B→E

13. 13 Weights of Data Sources Definition D i : data sources S i : set of association rules from D i R i : association rule 3 Steps Step 1 : union of all S i Step 2 : assigning each R i a weight Step 3 : assigning each D i a weight & normalization

14. 14 Example 3 Data Sources (minsupp=0.2, minconf=0.3) S 1 AB→C with supp=0.4, conf=0.72 A→D with supp=0.3, conf=0.64 B→E with supp=0.34, conf=0.7 S 2 B→C with supp=0.45, conf=0.87 A→D with supp=0.36, conf=0.7 B→E with supp=0.4, conf=0.6 S 3 AB→C with supp=0.5, conf=0.82 A→D with supp=0.25, conf=0.62

15. 15 Step 1 Union of all S i S’ = {S 1, S 2, S 3 } R 1 : AB→C S 1, S 3  2 times R 2 : A→D S 1, S 2, S 3  3 times R 3 : B→E S 1, S 2  2 times R 4 : B→C S 2  1 time

16. 16 Step 2 Assigning each R i a weight R 1 R 2 R 3 R 4 WR1 =WR1 = 2 + 3 + 2 + 1 2 = 0.25 WR2 =WR2 = 2 + 3 + 2 + 1 3 = 0.375 WR3 =WR3 = 2 + 3 + 2 + 1 2 = 0.25 WR4 =WR4 = 2 + 3 + 2 + 1 1 = 0.125

17. 17 Step 3 Assigning each D i a weight W D 1 2*0.25+3*0.375+2*0.25=2.125 W D 2 1*0.125+2*0.25+3*0.375=2 W D 3 2*0.25+3*0.375=1.625 Normalization W D 1  2.125/(2.125+2+1.625)=0.3695 W D 2  2/(2.125+2+1.625)=0.348 W D 3  1.625/(2.125+2+1.625)=0.2825

18. 18 Why Rule Selection ? Goal Extracting High-Frequency Rules Low-Frequency Rules  Noise Solution If Num(R i ) / n <  n : data sources, Num(R i ) : frequency of R i Then Rule R i  be wiped out

19. 19 Rule Selection Example : 10 Data Sources D 1 ~D 9 : {R 1 : X→Y} D 10 : {R 1 : X→Y, R 2 : X 1 →Y 1, …, R 11 : X 10 →Y 10 } Let  =0.8 Num(R 1 ) / 10 = 10/10 = 1 >   keep Num(R 2~11 ) / 10 = 1/10 = 0.1 <   be wiped out D 1 ~D 10 : {R 1 : X→Y} W R 1 : 10/10=1  W D 1~10 : 10*1 / 10*10*1 = 0.1 n Num(R 1 ) WR1WR1

20. 20 Comparison Without Rules Selection W D 1~9  0.099 W D 10  0.109 With Rules Selection W D 1~10  0.1 From High-Frequency Rules Point of view Weight Errors D 1~9  |0.1-0.099|  0.001 D 10  |0.1-0.109|  0.009 Total Error = 0.01

21. 21 Synthesizing High-Frequency Rules Algorithm 5 Steps Step 1 : Rules Selection Step 2 : Weights of Data Sources Step 2.1 : union of all S i Step 2.2 : assigning each R i a weight Step 2.3 : assigning each D i a weight & normalization Step 3 : computing supp & conf of each R i Step 4 : ranking all rules by support Step 5 : output the High-Frequency Rules

22. 22 An Example 3 Data Sources  =0.4, minsupp=0.2, minconf=0.3

23. 23 Step 1 Rules Selection R 1 : AB→C S 1, S 3  2 times Num(R 1 ) / 3 = 0.66  keep R 2 : A→D S 1, S 2, S 3  3 times Num(R 2 ) / 3 = 1  keep R 3 : B→E S 1, S 2  2 times Num(R 3 ) / 3 = 0.66  keep R 4 : B→C S 2  1 time Num(R 4 ) / 3 = 0.33  wiped out

24. 24 Step 2 : Weights of Data Sources Weights of R i Weight of D i W D 1  2*0.29+3*0.42+2*0.29=2.42 W D 2  3*0.42+2*0.29=1.84 W D 3  2*0.29+3*0.42=1.84 Normalization W D 1  2.42/(2.42+1.84+1.84)=0.3695=0.396 W D 2  1.84/(2.42+1.84+1.84)=0.302 W D 3  1.84/(2.42+1.84+1.84)=0.302 WR1 =WR1 = 2 + 3 + 2 2 = 0.29 WR2 =WR2 = 2 + 3 + 2 3 = 0.42 WR2 =WR2 = 2 + 3 + 2 2 = 0.29

25. 25 Step 3 Computing supp & conf of each R i Support AB  C 0.396*0.4+0.302*0.5=0.3094 A  D 0.396*0.3+0.302*0.36=0.228 B  E 0.396*0.34+0.302*0.4=0.255 Confidence AB  C 0.396*0.72+0.302*0.82=0.532 A  D 0.396*0.64+0.302*0.7=0.465 B  E 0.396*0.7+0.302*0.6=0.458 WD 1 =0.396 WD 2 =0.302 WD 3 =0.302

26. 26 Step 4 & Step 5 Ranking all rules by support & output minsupp=0.2, minconf=0.3 AB  C, B  E, A  D Ranking 1. AB  C (0.3094) 2. B  E (0.255) 3. A  D (0.228) Output – 3 rules AB  C(0.3094, 0.532) B  E (0.255, 0.458) A  D (0.228, 0.465)

27. 27 Internet Relative Synthesizing Model Framework Web books journals X→Y conf=0.7 X→Y conf=0.72 X→Y conf=0.68 X→Y conf=? Synthesizing clustering method roughly method Unknown D i

28. 28 Synthesizing Methods Physical Meaning if the confidences  irregularly distributed Maximum synthesizing operator Minimum synthesizing operator Average synthesizing operator if the confidences (X)  normal distribution clustering  interval [a, b] satisfy 1. P{ a  X  b } (m/n)  2. | b – a |   3. a, b > minconf.

29. 29 Clustering Method 5 Steps Step 1 : closeness  1 - | conf i – conf j | The distance relation table Step 2 : closeness degree measure The confidence-confidence matrix Step 3 : two confidences  close enough ?  The confidence relationship matrix Step 4 : classes creating [a, b]  interval of the confidence of rule X→Y Step 5 : interval verifying satisfy the constraints ?

30. 30 An Example Assume rule  X→Y conf 1 =0.7, conf 2 =0.72, conf 3 =0.68, conf 4 =0.5 conf 5 =0.71, conf 6 =0.69, conf 7 =0.7, conf 8 =0.91 3 parameters =0.7  =0.08  =0.69

31. 31 Step 1 : Closeness Example conf 1 =0.7, conf 2 =0.72 c 1, 2 = 1 - | conf 1 - conf 2 | = 1 - |0.70-0.72|=0.98

32. 32 Step 2 : Closeness Degree Measure Example         

33. 33 Step 3 : Close Enough ? Example  =6.9 > 6.9 < 6.9

34. 34 Step 4 : Classes Creating Example Class 1 : conf 1~3, conf 5~7 1 Class 2 : conf 4 Class 3 : conf 8 2 3

35. 35 Step 5 : Interval Verifying Example Class 1 conf 1 =0.7, conf 2 =0.72, conf 3 =0.68, conf 5 =0.71, conf 6 =0.69, conf 7 =0.7 [min, max] = [conf 3, conf 2 ] = [0.68, 0.72] constraint 1  P{ 0.68  X  0.72 } (6/8)  (0.7) constraint 2  |0.72-0.68| (0.04) <  (0.08) constraint 3  0.68, 0.75 > minconf. (0.65) In the same way Class 2 & Class 3  be wiped out Result  X→Y : conf=[0.68, 0.72] Support ? In the same way  Interval

36. 36 Roughly Method Example R : AB→C supp 1 =0.4, conf 1 =0.72 supp 2 =0.5, conf 2 =0.82 Maximum max ( supp (R) )=max (0.4, 0.5)=0.5 max ( conf (R) )=max (0.72, 0.82)=0.82 Minimum & Average min  0.4, 0.72 avg  0.45, 0.77

37. 37 Experiments Time SWNBS (without rules selection) SWBRS (with rules selection) SWNBS > SWBRS Error first 20 frequent itemset Max=0.000065 Avg=0.00003165

38. 38 Conclusion Synthesizing Model Data Sources  known weighting Data Sources  unknown clustering method roughly method

39. 39 Future works Sequence pattern Combine GA and other techniques