The document discusses the FP-Growth algorithm for frequent pattern mining. It improves upon the Apriori algorithm by not requiring candidate generation and only requiring two scans of the database. FP-Growth works by first building a compact FP-tree structure using two passes over the data, then extracting frequent itemsets directly from the FP-tree. An example is provided where an FP-tree is constructed from a sample transaction database and frequent patterns are generated from the tree. Advantages of FP-Growth include only needing two scans of data and faster runtime than Apriori, while a disadvantage is the FP-tree may not fit in memory.

1. Presented By:
Shihab Rahman
Dolon Chanpa
Department Of Computer Science And Engineering,
University of Dhaka

2.  FP Growth Stands for frequent pattern growth
 It is a scalable technique for mining frequent pattern
in a database

3.  FP growth improves Apriority to a big extent
 Frequent Item set Mining is possible without
candidate generation
 Only “two scan” to the database is needed
BUT HOW?

4.  Simply a two step procedure
– Step 1: Build a compact data structure called the FP-tree
• Built using 2 passes over the data-set.
– Step 2: Extracts frequent item sets directly from the FP-
tree

5.  Now Lets Consider the following transaction table
TID List of item IDs
T100 I1,I2,I3
T200 I2,I4
T300 I2,I3
T400 I1,I2,I4
T500 I1,I3
T600 I2,I3
T700 I1,I3
T800 I1,I2,I3,I5
T900 I1,I2,I3

6.  Now we will build a FP tree of that database
 Item sets are considered in order of their descending
value of support count.

7. null
I2:1
I1:1
I5:1
For Transaction:
I2,I1,I5

8. null
I2:2
I1:1
I5:1
I4:1
For Transaction:
I2,I4

9. null
I2:3
I1:1
I5:1
I3:1 I4:1
For Transaction:
I2,I3

10. null
I2:4
I1:2
I5:1
I3:1 I4:1
I4:1
For Transaction:
I2,I1,I4

11. null
I2:4
I1:2
I3:1 I4:1
I4:1
For Transaction:
I1,I3
I5:1
I1:1
I3:1

12. null
I2:5
I1:2
I3:2 I4:1
I4:1
For Transaction:
I2,I3
I5:1
I1:1
I3:1

13. null
I2:5
I1:2
I3:2 I4:1
I4:1
For Transaction:
I1,I3
I5:1
I1:2
I3:2

14. null
I2:6
I1:3
I3:1
I3:2
I5:1
I4:1
I4:1
For Transaction:
I2,I1,I3,I5
I5:1
I1:2
I3:2

15. null
I2:7
I1:4
I3:2
I3:2
I5:1
I4:1
I4:1
For Transaction:
I2,I1,I3
I1:2
I3:2
I5:1
Almost Over!

16. I2 7
I1 6
I3 6
I4 2
I5 2
null
I2:7
I1:4
I3:2
I3:2
I5:1
I4:1
I4:1
To facilitate tree traversal, an
item header table is built so
that each item points to its
occurrences in the tree via a
chain of node-links.
I1:2
I3:2
I5:1
FP Tree Construction Over!!
Now we need to find conditional pattern base
and Conditional FP Tree for each item

17. null
I2:7
I1:4
I3:2
I3:2
I5:1
I4:1
I4:1
I1:2
I3:2
I5:1
Conditional Pattern Base
I5 {{I2,I1:1},{I2,I1,I3:1}}
Conditional FP Tree for I5:{I2:2,I1:2}

18. null
I2:7
I1:4
I3:2
I3:2
I5:1
I4:1
I4:1
I1:2
I3:2
I5:1
Conditional Pattern Base
I4 {{I2,I1:1},{I2:1}}
Conditional FP Tree for I4:{I2:2}

19. null
I2:7
I14
I3:2
I3:2
I5:1
I4:1
I4:1
I1:2
I3:2
I5:1
Conditional Pattern Base
I3 {{I2,I1:2},{I2:2},{I1:2}}
Conditional FP Tree for I3:{I2:4,I1:2},{I1:2}

20. null
I2:7
I1:4
I3:2
I3:2
I5:1
I4:1
I4:1
I1:2
I3:2
I5:1
Conditional Pattern Base
I1 {{I2:4}}
Conditional FP Tree for I1:{I2:4}

21. Frequent Pattern Generated
I5 {I2, I5: 2}, {I1, I5: 2}, {I2, I1, I5: 2}
I4 {I2, I4: 2}
I3 {I2, I3: 4}, {I1, I3: 4}, {I2, I1, I3: 2}
I1 {I2, I1: 4}

22.  Advantages of FP-Growth
 only 2 passes over data-set
 “compresses” data-set
 no candidate generation
 much faster than Apriori
 Disadvantages of FP-Growth
 FP-Tree may not fit in memory!!
 FP-Tree is expensive to build
0
10
20
30
40
50
60
70
80
90
100
0 0.5 1 1.5 2 2.5 3
Support threshold(%)
Runtime(sec.)
D1 FP-grow th runtime
D1 Apriori runtime

23. Thank You