4. •
• [ =2%, =60%]
• A B
• A: (antecedent), B: (consequent)
• support
• A B
•
• confidence
• A B
•
5. (1/2)
TID item
• T100 I1, I2, I5
I = {I1 , I2 , ..., Im }
T200 I2, I4
• D T300 I2, I4
T T400 I1, I2, I4
• T T500 I1, I3
T ⊆I T600 I2, I3
T700 I1, I3
• T A
T800 I1, I2, I3, I5
A⊆T T900 I1, I2, I3
I = {I1, I2, I3, I4, I5}
• itemset
T100 : {I1, I2, I5}
• itemset k
k-itemset
6. (2/2) TID item
• A⇒B T100
T200
I1, I2, I5
I2, I4
A ⊂ I, B ⊂ I, A ∩ B = φ
T300 I2, I4
• A⇒B
T400 I1, I2, I4
support(A ⇒ B) = P (A ∪ B) T500 I1, I3
conf idence(A ⇒ B) = P (B | A) T600 I2, I3
T700 I1, I3
A = {I1} , B = {I2} , A ∪ B = {I1, I2} T800 I1, I2, I3, I5
P (A ∪ B) = 4/9 P (B | A) = 4/6 T900 I1, I2, I3
•
support(A ∪ B) support count(A ∪ B)
conf idence(A ⇒ B) = P (B | A) = =
support(A) support count(A)
15. • 1-itemset
DIC 2-itemset
S. Brin, R. Motowani, J. Ullman, and
S. Tsur. 1997
• {I2} {I4} min_sup
{12,14} . {12,I4}
min_sup
• DB
TID item Apriori DIC
T100 I1, I2, I5
T200 I2, I4
1-itemset
2-itemset
3-itemset
T300 I2, I4
1-itemset
2-itemset
3-itemset
T400 I1, I2, I4
T500 I1, I3
T600 I2, I3
T700 I1, I3
T800 I1, I2, I3, I5
T900 I1, I2, I3
16. •
• Hash
• (k+1)-itemset k-itemset
•
• PC
• Heap itemset
• FP-tree (J.Han, J. Pei and Y. Yin. 2000)
•
•
•
• S.Brin, R. Motwani and C. Silverstein. 1997
• S. Morishita and J. Sese. 2000