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Chaoyang University of Technology
Fuzzy data mining for interesting
generalized association rules
Source : Fuzzy Sets and Systems ; Vol.138, No. 2, 2003, pp.255-269
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
Outline
• Introduction
– Overview of Data Mining
– What is Association Mining
– Mining Procedures
•
•
•
•
Fuzzy Generalized Mining Algorithm
Experimental results
Conclusion
Comments
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Introduction(1/3)
Overview of Data Mining
Association Mining
AB equal to BA
Sequential Mining
ACDE
Classification
Clustering
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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.
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Introduction(3/3)
min. support = 2
Database D
TID
100
200
300
400
L2
Items
134
235
1235
25
itemset sup.
2
C1 {1}
{2}
3
Scan D {3}
3
{4}
1
{5}
3
itemset sup
{1 3}
2
{2 3}
2
{2 5}
3
{3 5}
2
C2
itemset sup
{1 2}
1
{1 3}
2
{1 5}
1
{2 3}
2
{2 5}
3
{3 5}
2
L1
itemset sup.
{1}
2
{2}
3
{3}
3
{5}
3
Scan D
C2
itemset
{1 2}
natural{1 3}
join
{1 5}
{2 3}
{2 5}
{3 5}
Self-join
C3
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itemset
{2 3 5}
Scan D
L3 itemset sup
{2 3 5}
2
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Fuzzy Generalized Mining Algorithm(1/12)
．Single concept level
TID
1
2
3
Items
Milk Cake
Juice T-shirt
Cake Milk
T-shirt
T-shirt
．Integrate fuzzy-set
TID
1
2
3
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Items
(Milk,3) (Cake,1)
(T-shirt,2)
(Juice,2) (T-shirt,1)
(Cake,1) (Milk,2) (T-shirt,1)
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Fuzzy Generalized Mining Algorithm(2/12)
．Multiple-level association rules
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Fuzzy Generalized Mining Algorithm(3/12)
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Fuzzy Generalized Mining Algorithm(4/12)
(C,9)(E,10)(T2,9)(T3,10)
Ex. (Bread,9) (T-shirt,10)
9
Food
( C , 9 ) => ( T2 , 9 )
10
Clothes
( E , 10 ) => ( T3 , 10 )
Drink
Bread
9
Jackets
Milk
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T-shirts
10
Juice
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Fuzzy Generalized Mining Algorithm(5/12)
0.8
0.6
0.5
0.4
0.2
9 10
(C,9) => C ( 0/Low , 0.4/Middle , 0.6/High )
(E,10) => E ( 0/Low , 0.2/Middle , 0.8/High )
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Fuzzy Generalized Mining Algorithm(6/12)
n
count jl   f ijl
i 1
,
count jl 為Membership的總和
C.High => 0.0 + 0.2 + 0.8 + 0.6 +0.0 + 0.4 = 2.0
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Fuzzy Generalized Mining Algorithm(7/12)
Minimum support value α = 1.5
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Fuzzy Generalized Mining Algorithm(8/12)
(T1.Low , T2.High)
ˇ (B.Low , C.Middle) ˇ (C.Middle , D.Middle) ˇ (D.Middle , T1.Low)
ˇ (B.Low , D.Middle) ˇ (C.Middle , T1.Low) ˇ (D.Middle , T2.High) ˇ (T1.Low , T3.Middle)
(B.Low , T1.Low)
(C.Middle , T2.High)
(B.Low , T2.High) ˇ (C.Middle , T3.Middle)
ˇ (B.Low , T3.Middle)
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(D.Middle , T3.Middle)
ˇ (T2.High , T3.Middle)
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Fuzzy Generalized Mining Algorithm(9/12)
Minimum support value α = 1.5
1.4
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Fuzzy Generalized Mining Algorithm(10/12)
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Confidence threshold 0.7
 (T .Middle  B.Low)
i 1
3
6
 (T .Middle )
i 1

1.6
 0.57
2.8
3
If B = Low , then T3 =Middle, with a confidence value of 0.73.
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Fuzzy Generalized Mining Algorithm(11/12)
Interest threshold 1.5
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Fuzzy Generalized Mining Algorithm(12/12)

If T1.Low , then T3.Middle , confidence factor of 0.8

If T3.Middle , then T2.High , confidence factor of 0.86
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Experimental results(1/3)
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Experimental results(2/3)
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Experimental results(3/3)
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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
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