1. Sequential Pattern Mining Using A Bitmap Representation
Authors: Jay Ayres, Johannes Gehrke,
Tomi Yiu and Jason Flannick
Source: The Eighth ACM SIGKDD International
Conference on Knowledge Discovery
and Data Mining, Edmonton, Alberta,
Canada, 2002.

2. Outline Introduction SPAM (Sequential PAttern mining) algorithm
Lexicographic tree for sequences
Depth first tree traversal
Pruning
S-step
I-step
Data representation - Bitmap

4. S= ({a}, {b, c}) is a sequence
The support of S is SupD(S)
Frequent sequential pattern:
SupD(S) >= Min Support
SupD(S) = SupD ({a}, {b, c} ) = 2

5. SPAM (Sequential pattern mining)
S = ({a, b, c}, {a, b})
Sequence length:
Length (S) = 5
Sequence size:
Size (S) = 2
Sequence-extended sequence
Itemset-extended sequence
S’ = ({a, b, c}, {a, b}, {a})
S’ = ({a, b, c}, {a, b, d})

6. SPAM (Sequential pattern mining)
Max Size = 3
Items = {a, b}
Level 1
Level 2
Level 3
Level 4
Level 5
Sequence-extended
Item-extended
Level 6

7. SPAM (Sequential pattern mining)
Max Size = 3
Items = {a, b}
Level 1
Level 2
Level 3
Level 4
Level 5
Level 6

8. SPAM (Sequential pattern mining)
Pruning
Items = {a, b, c, d}

9. Data Representation – BitMap
2K+1 < 3 < 2K+1

10. S-type
S = {a}
S’={a},{b}
S’={a},{c} …

11. I-type
S = {a}
S’={a, b}
S’={a, c} …

12. Expirations and results
D3 C2.5 T3
SPAM
SPADE
PrefixSpan

13. Small database Small database middle database middle database
SPADE
SPAM
PrefixSpan
prefix
middle database
middle database

14. large database

16. Conclusions SPAM DFS traversal search S-type I-type
Efficient in large database but inefficient in small database
Space-inefficient in comparison to SPADE