Mining Fuzzy Multiple-Level Association Rules from Quantitative Data

1. Mining Fuzzy Multiple-Level Association Rules from Quantitative Data Author: TZUNG-PEI HONG KUEI-YING LIN BEEN-CHIAN CHIEN Advisor: Dr. Hsu Graduate: Yan Pin Huang ADSL Wednesday, October 01, 2003

2. Content • Motivation • Objective • Introduction • Notation • The Multiple-Level Fuzzy Data-Mining Algorithm • Experimental Results • Conclusion • Personal opinion

3. Motivation • Machine-learning and data-mining techniques have been developed to turn data into useful task-oriented knowledge. • Most algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level.

4. Objective • This paper proposes a fuzzy multiple-level mining algorithm for extracting knowledge implicit in transactions stored as quantitative values. • The proposed algorithm adopts a top-down progressively deepening approach to finding large itemsets. • It integrates fuzzy-set concepts, data-mining technologies and multiple-level taxonomy to find fuzzy association rules from transaction data sets.

5. Introduction • Proposed a method for mining association rules from data sets using quantita-tive and categorical attributes. • R. Srikant and R. Agrawal, “Mining quantitative association rules in large relational tables,” in The 1996 ACM SIGMOD International Conference on Management of Data.

6. Introduction(cont.) • Fuzzy set theory is being used more and more fre-quently in intelligent systems because of its simplicity and similarity to human reasoning [15].

7. Mining at Multiple Concept Levels • They divided the min-ing process into two phases. • Candidate itemsets were generated and counted by scanning the transaction data. • Association rules were induced from the large itemsets found in the first phase

8. Mining at Multiple Concept Levels

9. Notation(cont.)

10. Notation(cont.)

11. The Multiple-Level FuzzyData-Mining Algorithm

12. The Multiple-Level FuzzyData-Mining Algorithm(cont) • Step 1. Each item name is first encoded using the predefined taxonomy. Results are shown in Table 2.

13. The Multiple-Level FuzzyData-Mining Algorithm(cont) 1** 11* 111

14. The Multiple-Level FuzzyData-Mining Algorithm(cont) • Step 2. All transactions shown in Table 1 are then encoded using the above coding scheme • Step 3. k is initially set at 1, where k is used to store the level number being processed. • Step 4. All the items in the transactions are first grouped on level one.

15. The Multiple-Level FuzzyData-Mining Algorithm(cont)

16. The Multiple-Level FuzzyData-Mining Algorithm(cont) • Step 5. The quantitative values of the items on level 1 are represented using fuzzy sets.

17. The Multiple-Level FuzzyData-Mining Algorithm(cont) • (1,1)(6,0) 帶入 y=ax+b 求解a,b (a=-1/5 b=6/5) • Function: y= -1/5x+6/5 • (1**,5)帶入member function求得 y=0.2

18. The Multiple-Level FuzzyData-Mining Algorithm(cont) • Step 6. The scalar cardinality of each fuzzy region in the transactions is calculated as the count value. • Its scalar cardinality=(0.8+0.8+ 0.0+0.2+0.0+0.0) =1.8

19. The Multiple-Level FuzzyData-Mining Algorithm(cont) • Step 7. The fuzzy region with the highest count among the three possible regions for each item is found.

20. The Multiple-Level FuzzyData-Mining Algorithm(cont) • Step 8. The count of any region selected in Step 7 is checked against the predefined minimum support value α. (α=2.1)

21. The Multiple-Level FuzzyData-Mining Algorithm(cont) 1.2

22. The Multiple-Level FuzzyData-Mining Algorithm(cont) • Step 12. r is set at 2, where r is used to store the number of items kept in the current itemsets. • Step 13. Since is null, k =k + 1=2 and Step 4 is done. The results for level 2 are shown in Table 10.The results for level 3 are shown in Table 11.Since there are no items on level 4, Step 17 is done.

23. The Multiple-Level FuzzyData-Mining Algorithm(cont) • Step 17. The association rules are constructed for each large itemset using the following substeps.

24. The Multiple-Level FuzzyData-Mining Algorithm(cont)

25. The Multiple-Level FuzzyData-Mining Algorithm(cont) • Step 18. The confidence values of the possible association rules are checked against the predefined confidence threshold λ. (λ =0.7)

26. 6. Experimental Results They were implemented in C on a Pentium-III 700 Personal Computer. The number of levels was set at 3. 64 purchased items (terminal nodes) on level 3, 16generalized items on level 2, and 4 generalized items on level 1.

27. Experimental Results(cont)

28. Experimental Results(cont)

29. Discussion and Conclusions • Proposed a fuzzy multiplelevel data-mining algorithm that can process transaction data with quantitative values and discover interesting patterns among them. • This method achieves better time complexity since only the most important fuzzy term is used for each item. • This proposed algorithm does not find association rules for items on the same paths in given hierarchy trees.

30. Discussion and Conclusions • We will therefore attempt to dynamically adjust the membership functions in the proposed mining algorithm • We will also attempt to design specific data-mining models for various problem domains.

31. Personal opinion • Find association rules for items on the same paths in given hierarchy trees. • Find a method that can dynamically adjust the membership functions. • Fuzzy SOM. • Fuzzy clustering. • The strategy of using fuzzy set in Ant Colony algorithm.

32. Personal opinion(cont.)

33. Personal opinion(cont.)