2. Frequent Pattern  交易資料庫中頻繁的被一起購買的產品  可以做為推薦產品、銷售決策的依據  兩大演算法 Apriori FP-Tree

3. Apriori Transaction IDItems Bought 11 3 4 22 3 5 31 2 3 5 42525 Large 1 itemsets 1 2 3 5 Candidate 1Count 12 23 33 41 53 scan count

4. Apriori C2C2 12 13 15 23 25 35 L 1 1 2 3 5 C2C2 Count 121 132 151 232 253 352 scan count generate C 2 TIDItems Bought 11 3 4 22 3 5 31 2 3 5 42525 L 2 13 23 25 35

5. Apriori C3C3 235 L 2 13 23 25 35 C3C3 Count 2352 scan count generate C 3 TIDItems Bought 11 3 4 22 3 5 31 2 3 5 42525 L 3 235 All frequent itemsets 13 235

6. Apriori 缺點  效率不佳 IO 時間過長 Candidate 過多

8. FP-Tree  min_sup= 50%  min_sup_count= 3 TIDItems Bought 100f,a,c,d,g,i,m,p 200a,b,c,f,l,m,o 300b,f,h,j,o 400b,c,k,s,p 500a,f,c,e,l,p,m,n Itemssupport abcdefghijklmnopabcdefghijklmnop 33411411111231233341141111123123 Itemssupport fcabmpfcabmp 443333443333

9. FP-Tree Itemssupport fcabmpfcabmp 443333443333 TIDItems BoughtFrequent Items 100f,a,c,d,g,i,m,pf,c,a,m,p 200a,b,c,f,l,m,of,c,a,b,m 300b,f,h,j,of,b 400b,c,k,s,pc,b,p 500a,f,c,e,l,p,m,nf,c,a,m,p

10. itemhead of node-links f c a b m p Root (order) Frequent Items f,c,a,m,p f,c,a,b,m f,b c,b,p f,c,a,m,p f:1 fc c:1 a:1 m:1 p:1 f f:2 c c:2 a a:2 b b:1 m:1 m

11. itemhead of node-links f c a b m p Root (order) Frequent Items f,c,a,m,p f,c,a,b,m f,b c,b,p f,c,a,m,p f:4 c:3 a:3 m:2 p:2 b:1 m:1 b:1 c:1 b:1 p:1

12. itemhead of node-links f c a b m p Root f:4 c:3 a:3 m:2 p:2 b:1 m:1 b:1 c:1 b:1 p:1 Prefix {m}-Conditional Pattern Base m( acf:2 ) (bacf:1 )

13. Frequent items | p f:3 c:3 a:3 Root f:3 itemhead of node-links f c a Prefix {m}-Conditional Pattern Base m( acf:2 ) (bacf:1 ) c:3 a:3 Frequent Item Sets fm:3, cm:3, am:3, fcm:3, fam:3, cam:3, fcam:3

14. itemhead of node-links f c a b m p Root f:4 c:3 a:3 m:2 p:2 b:1 m:1 b:1 c:1 b:1 p:1 Frequent Item Sets cp:3, am:3, cam:3, fcam:3, fcam:3, fam:3, cm:3, fcm:3, fm:3, ca:3, fca:3, fa:3, fc:3