1. ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis)
Sequential Pattern Mining
Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential Chair of Business

2. Given: A Transaction Database { cid, tid, date, item }
Sequential Patterns
Given: A Transaction Database { cid, tid, date, item }
Find: inter-transaction patterns among customers
Example: customers typically rent “ Star Wars”, then “Empire Strikes Back” and then “Return of the Jedi”
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.

3. Sequential Patterns cid tid date item 1 1 01/01/2000 30
/01/
/02/
/01/ ,70
/02/
/03/ ,60,70
/01/ ,50,70
/01/
/02/ ,70
/03/
/01/
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.

4. Itemset : is a non-empty set of items, e.g., {30} , {40, 70}.
Sequential Patterns
Itemset : is a non-empty set of items,
e.g., {30} , {40, 70}.
Sequence: is an ordered list of itemsets,
e.g. <{30} {40,70}> , <{40,70} {30} >.
Size of sequence is the number of itemsets in that sequence.
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.

5. Each transaction of a customer can be viewed as an itemset
Sequential Patterns
cid tid date item
/01/
/02/
/01/ ,70
/02/
/03/ ,60,70
/01/ ,50,70
/01/
/02/ ,70
/03/
/01/
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.
Each transaction of a customer can be viewed as an itemset
A customer’s sequences contains the customer’s ordered itemsets

6. Sequential Patterns cid customer sequence 1 <{30} {90} >
<{30} {90} >
<{40,70} {30} {40,60,70}>
<{30,50,70}>
<{30} {40,70} {90}>
<{90}>
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.

7. E.g., <{3} {4,5} {8}> is contained in < {3,8}{4,5,6} {8}>
Sequential Patterns
Sequence <a1 a2 ….an> is contained in sequence <b1 b2 ….bm> if there exist indexes i1<i2….<in such that
a1 bi1, a2 bi2, …, and an bin.
E.g., <{3} {4,5} {8}> is contained in < {3,8}{4,5,6} {8}>
Is <{3} {4,5} {8}> contained in <{7} {3,8} {9}{4,5,6} {8}> ?
Is <{3} {4,5} {8}> contained in <{7} {9} {4,5,6} {3,8} {8}> ?
Is <{3} {4,5} {8}> contained in <{7} {9} {3,8}{4,5,6} > ?
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.

8. A customer supports sequence s if s is contained in the
Sequential Patterns
cid customer sequence
<{30} {90} >
<{40,70} {30} {40,60,70}>
<{30,50,70}>
<{30} {40,70} {90}>
<{90}>
A customer supports sequence s if s is contained in the
sequence for this customer.
E.g., customers 1 and 4 support sequence <{30} {90}>
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.

9. The support for a sequence s is defined as the fraction of
Sequential Patterns
cid customer sequence
<{30} {90} >
<{40,70} {30} {40,60,70}>
<{30,50,70}>
<{30} {40,70} {90}>
<{90}>
The support for a sequence s is defined as the fraction of
total customers who support s .
E.g., customers 1 and 4 support sequence <{30} {90}>
Supp(<{30} {90}>) = 2/5 = 40%
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.

10. Supp(<{40,70}>) = 2/5 = 40% Supp({40,70}) = 3/10 = 30%
Sequential Patterns
cid customer sequence
<{30} {90} >
<{40,70} {30} {40,60,70}>
<{30,50,70}>
<{30} {40,70} {90}>
<{90}>
Supp(<{40,70}>) = 2/5 = 40%
Supp({40,70}) = / = 30%
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.

11. Sequential Patterns Mining
Given: A Transaction Database { cid, tid, date, item }
Find: All sequences that have support larger than user-specified minimum support
Apriori property: if a sequence is large then all sequences contained in that sequence should be large.
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.

12. Sequential Patterns Mining
Identify all Large 1-Sequences
Repeat until there is no more Candidate k-Sequences
Identify all Candidate k-Sequences using Large (k-1)-Sequences
Join:Two large (k-1)-sequences, L1 amd L2, that are joinable
must satisfy the following conditions:
L1(1)=L2(1) and L1(2)=L2(2) and …. L1(K-2)=L2(K-2)
L1(K-1) L2(K-1)
Prune :prune candidate k-sequences generated in step 2-1
that have sub-sequences not large.
Determine Large k-Sequences from Candidate k-Sequences

13. Sequential Patterns Mining
cid customer sequence
<{30} {90} >
<{40,70} {30} {40,60,70}>
<{30,50,70}>
<{30} {40,70} {90}>
<{90}>
Minimum Support: 40%
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.

14. Sequential Patterns Mining
cid customer sequence
1 <{30} {90} >
2 <{40,70} {30} {40,60,70}>
3 <{30,50,70}>
4 <{30} {40,70} {90}>
5 <{90}>
Minimum Support: 40%
Large 1-Sequence:
<{30}> support=4/5=80%
<{40}> support=2/5=40%
<{70}> support=3/5=60%
<{90}> support=3/5=60%
<{40,70}> support=2/5=40%
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.

15. Sequential Patterns Mining
Large 1-Sequence:
<{30}> support=4/5=80%
<{40}> support=2/5=40%
<{70}> support=3/5=60%
<{90}> support=3/5=60%
<{40,70}> support=2/5=40%
Candidate 2-Sequence:
<{30} {40}> <{30} {70}> <{30} {90}> <{30} {40,70}>
<{40} {30}> <{40} {70}> <{40} {90}> <{40} {40,70}>
<{70} {30}> <{70} {40}> <{70} {90}> <{70} {40,70}>
<{90} {30}> <{90} {40}> <{90} {70}> <{90} {40,70}>
<{40,70} {30}> <{40,70} {40}> <{40,70} {70}> <{40,70} {90}>
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.

16. Sequential Patterns Mining
Candidate 2-Sequence:
<{30} {40}> <{30} {70}> <{30} {90}> <{30} {40,70}>
<{40} {30}> <{40} {70}> <{40} {90}> <{40} {40,70}>
<{70} {30}> <{70} {40}> <{70} {90}> <{70} {40,70}>
<{90} {30}> <{90} {40}> <{90} {70}> <{90} {40,70}>
<{40,70} {30}> <{40,70} {40}> <{40,70} {70}> <{40,70} {90}>
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.
Large 2-Sequence:
<{30} {40}> support=2/5=40%
<{30} {70}> support=2/5=40%
<{30} {90}> support=2/5=40%
<{30} {40,70}> support=2/5=40%

17. Sequential Patterns Mining
Large 2-Sequence:
<{30} {40}> support=2/5=40%
<{30} {70}> support=2/5=40%
<{30} {90}> support=2/5=40%
<{30} {40,70}> support=2/5=40%
Candidate 3-Sequence:
<{30} {40} {70}> <{30} {40} {40,70}>
<{30} {70} {40}> <{30} {70} {40,70}>
<{30} {40,70} {40}> <{30} {40,70} {70}>
<{30} {40} {90}> <{30} {90} {40}>
<{30} {70} {90}> <{30} {90} {70}>
<{30} {90} {40,70}> <{30} {40,70} {90}>
Prune:
All sub-sequences of a candidate k-sequence should be large.
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.
Candidate 3-Sequence:
No candidate 3-sequence. Stop.

18. What is a sequential pattern?
Summary
What is a sequential pattern?
What is support for a sequential pattern?
How to mine sequential patterns?
What are the similarities and dissimilarities between association rules and sequential patterns mining?
Part 1: Research Area (1 slide, 3-3)
Lots of research have been done to solve the first two problems. Little has been done for the third problem -- incremental data mining.