Fuzzy data mining for interesting generalized association rules Source : Fuzzy Sets and Systems ; Vol.138, No. 2, 2003, pp Author : Tzung-Pei, Kuei-Ying Lin, Shyue-Liang Wang Instructor Professor :Rong-Chung Chen Present : Ya-Hui Chin ( 金雅慧 ) Chaoyang University of Technology

2005/12/06 Fuzzy data mining for interesting generalized association rules 2 Outline Introduction –Overview of Data Mining –What is Association Mining –Mining Procedures Fuzzy Generalized Mining Algorithm Experimental results Conclusion Comments Chaoyang University of Technology

2005/12/06 Fuzzy data mining for interesting generalized association rules 3 Introduction(1/3) Chaoyang University of Technology Overview of Data Mining Association Mining A  B equal to B  A Sequential Mining A  C  D  E Classification Clustering

2005/12/06 Fuzzy data mining for interesting generalized association rules 4 Introduction(2/3) What is Association Mining Finding frequent patterns, correlations, or causal etc. Application –Market basket analysis, cross-marketing, catalog design, clustering, classification, etc. Chaoyang University of Technology

2005/12/06 Fuzzy data mining for interesting generalized association rules 5 Introduction(3/3) Chaoyang University of Technology Database D L3L3 Scan D L2L2 L1L1 C2C2 C3C3 Self-join Scan D C1C1 min. support = 2 C2C2 natural- join

2005/12/06 Fuzzy data mining for interesting generalized association rules 6 Fuzzy Generalized Mining Algorithm (1/12) TIDItems 1 MilkCakeT-shirt 2 JuiceT-shirt 3 CakeMilkT-shirt Chaoyang University of Technology TIDItems 1 (Milk,3)(Cake,1)(T-shirt,2) 2 (Juice,2)(T-shirt,1) 3 (Cake,1)(Milk,2)(T-shirt,1) ． Single concept level ． Integrate fuzzy-set

2005/12/06 Fuzzy data mining for interesting generalized association rules 7 Fuzzy Generalized Mining Algorithm (2/12) Chaoyang University of Technology ． Multiple-level association rules

2005/12/06 Fuzzy data mining for interesting generalized association rules 8 Fuzzy Generalized Mining Algorithm (3/12) Chaoyang University of Technology

2005/12/06 Fuzzy data mining for interesting generalized association rules 9 Fuzzy Generalized Mining Algorithm (4/12) Chaoyang University of Technology MilkJuice Drink Bread Food Jackets T-shirts Clothes (C,9)(E,10)(T 2,9)(T 3,10) ( C, 9 ) => ( T 2, 9 ) ( E, 10 ) => ( T 3, 10 ) Ex. (Bread,9) (T-shirt,10)

2005/12/06 Fuzzy data mining for interesting generalized association rules 10 Fuzzy Generalized Mining Algorithm (5/12) Chaoyang University of Technology (C,9) => C ( 0/Low, 0.4/Middle, 0.6/High ) (E,10) => E ( 0/Low, 0.2/Middle, 0.8/High )

2005/12/06 Fuzzy data mining for interesting generalized association rules 11 Fuzzy Generalized Mining Algorithm (6/12) Chaoyang University of Technology C.High => = 2.0, 為 Membership 的總和

2005/12/06 Fuzzy data mining for interesting generalized association rules 12 Fuzzy Generalized Mining Algorithm (7/12) Chaoyang University of Technology Minimum support value α = 1.5

2005/12/06 Fuzzy data mining for interesting generalized association rules 13 Fuzzy Generalized Mining Algorithm (8/12) Chaoyang University of Technology (B.Low, C.Middle) (B.Low, D.Middle) (B.Low, T 1.Low) (B.Low, T 2.High) (B.Low, T 3.Middle) ˇ (C.Middle, D.Middle) (C.Middle, T 1.Low) (C.Middle, T 2.High) (C.Middle, T 3.Middle) (D.Middle, T 1.Low) (D.Middle, T 2.High) (D.Middle, T 3.Middle) (T 1.Low, T 2.High) (T 1.Low, T 3.Middle) (T 2.High, T 3.Middle) ˇ ˇ ˇ ˇ ˇ ˇ ˇˇ ˇ

2005/12/06 Fuzzy data mining for interesting generalized association rules 14 Fuzzy Generalized Mining Algorithm (9/12) Chaoyang University of Technology 1.4 Minimum support value α = 1.5

2005/12/06 Fuzzy data mining for interesting generalized association rules 15 Fuzzy Generalized Mining Algorithm (10/12) Chaoyang University of Technology If B = Low, then T 3 =Middle, with a confidence value of Confidence threshold 0.7

2005/12/06 Fuzzy data mining for interesting generalized association rules 16 Fuzzy Generalized Mining Algorithm (11/12) Chaoyang University of Technology Interest threshold 1.5

2005/12/06 Fuzzy data mining for interesting generalized association rules 17 Fuzzy Generalized Mining Algorithm (12/12) Chaoyang University of Technology  If T 1.Low, then T 3.Middle, confidence factor of 0.8  If T 3.Middle, then T 2.High, confidence factor of 0.86

2005/12/06 Fuzzy data mining for interesting generalized association rules 18 Experimental results(1/3) Chaoyang University of Technology

2005/12/06 Fuzzy data mining for interesting generalized association rules 19 Experimental results(2/3) Chaoyang University of Technology

2005/12/06 Fuzzy data mining for interesting generalized association rules 20 Experimental results(3/3) Chaoyang University of Technology

2005/12/06 Fuzzy data mining for interesting generalized association rules 21 Conclusion & Comments Conclusion –Discovering interesting patterns –Getting smoother mining rules Comments –Without explaining how to classify the items –Without comparing with other method in experiment Chaoyang University of Technology