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# Data mining fp growth

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A simple graphical presentation of the implementation of FP Growth Algorithm for mining frequent pattern in a database

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### Data mining fp growth

1. 1. Presented By:Shihab RahmanDolon ChanpaDepartment Of Computer Science And Engineering,University of Dhaka
2. 2.  FP Growth Stands for frequent pattern growth It is a scalable technique for mining frequent patternin a database
3. 3.  FP growth improves Apriority to a big extent Frequent Item set Mining is possible withoutcandidate generation Only “two scan” to the database is neededBUT HOW?
4. 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. 5.  Now Lets Consider the following transaction tableTID List of item IDsT100 I1,I2,I3T200 I2,I4T300 I2,I3T400 I1,I2,I4T500 I1,I3T600 I2,I3T700 I1,I3T800 I1,I2,I3,I5T900 I1,I2,I3
6. 6.  Now we will build a FP tree of that database Item sets are considered in order of their descendingvalue of support count.
7. 7. nullI2:1I1:1I5:1For Transaction:I2,I1,I5
8. 8. nullI2:2I1:1I5:1I4:1For Transaction:I2,I4
9. 9. nullI2:3I1:1I5:1I3:1 I4:1For Transaction:I2,I3
10. 10. nullI2:4I1:2I5:1I3:1 I4:1I4:1For Transaction:I2,I1,I4
11. 11. nullI2:4I1:2I3:1 I4:1I4:1For Transaction:I1,I3I5:1I1:1I3:1
12. 12. nullI2:5I1:2I3:2 I4:1I4:1For Transaction:I2,I3I5:1I1:1I3:1
13. 13. nullI2:5I1:2I3:2 I4:1I4:1For Transaction:I1,I3I5:1I1:2I3:2
14. 14. nullI2:6I1:3I3:1I3:2I5:1I4:1I4:1For Transaction:I2,I1,I3,I5I5:1I1:2I3:2
15. 15. nullI2:7I1:4I3:2I3:2I5:1I4:1I4:1For Transaction:I2,I1,I3I1:2I3:2I5:1Almost Over!
16. 16. I2 7I1 6I3 6I4 2I5 2nullI2:7I1:4I3:2I3:2I5:1I4:1I4:1To facilitate tree traversal, anitem header table is built sothat each item points to itsoccurrences in the tree via achain of node-links.I1:2I3:2I5:1FP Tree Construction Over!!Now we need to find conditional pattern baseand Conditional FP Tree for each item
17. 17. nullI2:7I1:4I3:2I3:2I5:1I4:1I4:1I1:2I3:2I5:1Conditional Pattern BaseI5 {{I2,I1:1},{I2,I1,I3:1}}Conditional FP Tree for I5:{I2:2,I1:2}
18. 18. nullI2:7I1:4I3:2I3:2I5:1I4:1I4:1I1:2I3:2I5:1Conditional Pattern BaseI4 {{I2,I1:1},{I2:1}}Conditional FP Tree for I4:{I2:2}
19. 19. nullI2:7I14I3:2I3:2I5:1I4:1I4:1I1:2I3:2I5:1Conditional Pattern BaseI3 {{I2,I1:2},{I2:2},{I1:2}}Conditional FP Tree for I3:{I2:4,I1:2},{I1:2}
20. 20. nullI2:7I1:4I3:2I3:2I5:1I4:1I4:1I1:2I3:2I5:1Conditional Pattern BaseI1 {{I2:4}}Conditional FP Tree for I1:{I2:4}
21. 21. Frequent Pattern GeneratedI5 {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. 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 build01020304050607080901000 0.5 1 1.5 2 2.5 3Support threshold(%)Runtime(sec.)D1 FP-grow th runtimeD1 Apriori runtime
23. 23. Thank You