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شرح ال FP Growth data mining algorithm.pptx
 FP Growth Stands for frequent pattern growth
 It is a scalable technique for mining frequent pattern
in a database
 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?
شرح ال FP Growth data mining algorithm.pptx
 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
 Now Lets Consider the following transaction table
TID List of item IDs
T100
T200
T300
T400
T500
T600
T700
T800
T900
I1,I2,I5
I2,I4
I2,I3
I1,I2,I4
I1,I3
I2,I3
I1,I3
I1,I2,I3,I5
I1,I2,I3
 Now we will build a FP tree of that database
 Item sets are considered in order of their descending
value of support count.
Items count
I1 6
I2 7
I3 6
I4 2
I5 2
1. Count of each item
Items count
I2 7
I1 6
I3 6
I4 2
I5 2
2. Sort the itemset in descending order.
 Now Lets Consider the following transaction table
TID List of item IDs
T100
T200
T300
T400
T500
T600
T700
T800
T900
I2,I1,I5
I2,I4
I2,I3
I2, I1, I4
I1,I3
I2,I3
I1,I3
I2, I1, I3,I5
I2, I1, I3
For Transaction:
I2,I1,I5 null
I2:1
I1:1
I5:1
For Transaction:
I2,I4 null
I2:2
I1:1
I4:1
I5:1
For Transaction:
I2,I3 null
I2:3
I3:1
I1:1
I4:1
I5:1
For Transaction:
I2,I1,I4 null
I2:4
I3:1
I1:2
I4:1
I4:1
I5:1
For Transaction:
I1,I3 null
I2:4
I3:1
I1:1
I1:2
I4:1
I3:1
I5:1
I4:1
For Transaction:
I2,I3 null
I2:5
I3:2
I1:1
I1:2
I4:1
I3:1
I5:1
I4:1
For Transaction:
I1,I3 null
I2:5
I3:2
I1:2
I1:2
I4:1
I3:2
I5:1
I4:1
For Transaction:
I2,I1,I3,I5 null
I2:6
I3:2
I1:2
I1:3
I4:1
I3:2
I5:1
I4:1
I3:1
I5:1
For Transaction:
I2,I1,I3 null
I2:7
I3:2
I1:2
I1:4
I4:1
I3:2
I5:1
I4:1
I3:2
Almost Over!
I5:1
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.
null
I2:7
I3:2
I2
I1
7
6
6
2
2
I1:2
I3
I4
I5
I1:4
I4:1
I3:2
I5:1
I4:1
FP Tree Construction Over!!
I3:2
Now we need to find conditional pattern base
and Conditional FP Tree for each item
I5:1
Conditional Pattern Base
I5 {{I2,I1:1},
{I2,I1,I3:1}}
null
I2:7
I1:2
I1:4
I3:2
I4:1
I4:1
I3:2
I5:1
I3:2
Conditional FP Tree for I5:{I2:2,I1:2}
I5:1
Conditional Pattern Base
I4 {{I2,I1:1},{I2:1}}
null
I2:7
I3:2
I1:2
I1:4
I4:1
I3:2
I5:1
I4:1
I3:2
Conditional FP Tree for I4:{I2:2}
I5:1
Conditional Pattern Base
I3 {{I2,I1:2},{I2:2},
{I1:2}}
null
I2:7
I3:2
I1:2
I14
I4:1
I3:2
I5:1
I4:1
I3:2
Conditional FP Tree for I3:{I2:4,I1:2},{I1:2
I5:1
Conditional Pattern Base
I1 {{I2:4}} null
I2:7
I3:2
I1:2
I1:4
I4:1
I3:2
I5:1
I4:1
I3:2
Conditional FP Tree for I1:{I2:4}
I5:1
شرح ال FP Growth data mining algorithm.pptx
Frequent Pattern Generated
I5
I4
{I2, I5: 2}, {I1, I5: 2}, {I2, I1, I5: 2}
{I2, I4: 2}
I3
I1
{I2, I3: 4}, {I1, I3: 4}, {I2, I1, I3: 2}
{I2, I1: 4}
100
90
80
70
60
50
40
30
20
10
0
D1 FP- gr ow th r untime
D1 A priori r untime
0 0.5 1 1.5 2 2.5 3
Support thre shold(%)
Thank Yo u

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شرح ال FP Growth data mining algorithm.pptx