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Mining Frequent Patterns Using FP-Growth Method Ivan Tanasić Department of Computer Engineering and Computer Science, School of Electrical.

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Presentation on theme: "Mining Frequent Patterns Using FP-Growth Method Ivan Tanasić Department of Computer Engineering and Computer Science, School of Electrical."— Presentation transcript:

1 Mining Frequent Patterns Using FP-Growth Method Ivan Tanasić (itanasic@gmail.com) Department of Computer Engineering and Computer Science, School of Electrical Engineering, University of Belgrade

2 Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach ◦ Jiawei Han (UIUC) ◦ Jian Pei (Buffalo) ◦ Yiwen Yin (SFU) ◦ Runying Mao (Microsoft) Ivan Tanasic (itanasic@gmail.com)2/25

3 Problem Definition Mining frequent patterns from a DB ◦ Frequent intemsets  (milk + bread) ◦ Frequent sequential patterns  (computer -> printer -> paper) ◦ Frequent structural patterns  (subgraphs, subtrees) Ivan Tanasic (itanasic@gmail.com)3/25

4 Problem Importance 1/2 Basic DM primitive Used for mining data relationships ◦ Associations ◦ Correlations Helps with basic DM tasks ◦ Classification ◦ Clustering Ivan Tanasic (itanasic@gmail.com)4/25

5 Problem importance 2/2 Association rules ◦ buys(“laptop”)=>buys(“mouse”) [support = 2%, confidence = 30%] Ivan Tanasic (itanasic@gmail.com) Support=% of all transactions containing that items Confidence=% of transactions containing I1 that contain I2 5/25

6 Problem Trend Apriori speedup using techniques New data structures (trees) Association rule specific algorithms Specific AR algorithms (OneR, ZeroR) FP-Growth still widely used Ivan Tanasic (itanasic@gmail.com)6/25

7 Existing Solutions 1/3 (Apriori) Agrawal et al. (1994) AP: All nonempty subsets of a frequent itemset must also be frequent Starts from 1-itemsets Join + prune (using AP + min supp) Generates huge number of candidates Ivan Tanasic (itanasic@gmail.com)7/25

8 Existing Solutions 2/3 (ECLAT) Zaki (2000) Equivalence CLass Transformation Vertical format: {item,TID_set} instead of {TID,itemset} Intersects TID_sets of candidates TID_sets holds support info (no scans) Still generates candidates Ivan Tanasic (itanasic@gmail.com)8/25

9 Existing Solutions 3/3 (TreeProjection) Agarwal et al. (2001) Creates a lexicographical tree and projects db into sub-dbs based on the patterns mined so far Recursively mines subdatabases Less scalable then FP-Growth Ivan Tanasic (itanasic@gmail.com)9/25

10 FP-Tree construction 1/6 Ivan Tanasic (itanasic@gmail.com) Desc. supp. sort Min support = 2 10/25

11 FP-Tree construction 2/6 Ivan Tanasic (itanasic@gmail.com) Desc. supp. sort T1={I2,I1,I5} 11/25

12 FP-Tree construction 3/6 Ivan Tanasic (itanasic@gmail.com) Desc. supp. sort T1 = {I2, I1, I5} T2 = {I2, I4} 12/25

13 FP-Tree construction 4/6 Ivan Tanasic (itanasic@gmail.com) Desc. supp. sort T1 = {I2, I1, I5} T2 = {I2, I4} T3 = {I2, I3} 13/25

14 FP-Tree construction 5/6 Ivan Tanasic (itanasic@gmail.com) Desc. supp. sort T1 = {I2, I1, I5} T2 = {I2, I4} T3 = {I2, I3} T4 = {I2, I1, I4} 14/25

15 FP-Tree construction 6/6 Ivan Tanasic (itanasic@gmail.com) Desc. supp. sort T1 = {I2, I1, I5} T2 = {I2, I4} T3 = {I2, I3} T4 = {I2, I1, I4} T5 = {I1, I3} T6 = {I2, I3} T7 = {I1, I3} T8 = {I2, I1, I3, I5} T9 = {I2, I1, I3} 15/25

16 Mining of the FP-Tree 1/4 Ivan Tanasic (itanasic@gmail.com) It.Conditional P. baseCond. FP-TreeFreq. Patterns Generated I5{{I2,I1:1},{I2,I1,I3:1}}{I2:2, I1:2}{I2,I5:2},{I1,I5:2},{I2,I1,I5:2} 16/25

17 Mining of the FP-Tree 2/4 Ivan Tanasic (itanasic@gmail.com) It.Conditional P. baseCond. FP-TreeFreq. Patterns Generated I5{{I2,I1:1},{I2,I1,I3:1}}{I2:2, I1:2}{I2,I5:2},{I1,I5:2},{I2,I1,I5:2} I4{{I2,I1:1},{I2:1}}{I2:2}{I2,I4:2} 17/25

18 Mining of the FP-Tree 3/4 Ivan Tanasic (itanasic@gmail.com) It.Conditional P. baseCond. FP-TreeFreq. Patterns Generated I5{{I2,I1:1},{I2,I1,I3:1}}{I2:2, I1:2}{I2,I5:2},{I1,I5:2},{I2,I1,I5:2} I4{{I2,I1:1},{I2:1}}{I2:2}{I2,I4:2} I3{{I2,I1:2},{I2:2},{I1:2}}{I2:4,I1:2},{I1:2}{I2,I3:4},{I1,I3:4},{I2,I1,I3:2} 18/25

19 Mining of the FP-Tree 4/4 Ivan Tanasic (itanasic@gmail.com) It.Conditional P. baseCond. FP-TreeFreq. Patterns Generated I5{{I2,I1:1},{I2,I1,I3:1}}{I2:2, I1:2}{I2,I5:2},{I1,I5:2},{I2,I1,I5:2} I4{{I2,I1:1},{I2:1}}{I2:2}{I2,I4:2} I3{{I2,I1:2},{I2:2},{I1:2}{I2:4,I1:2},{I1:2}{I2,I3:4},{I1,I3:4},{I2,I1,I3:2} I1{{I2:4}}{I2:4}{I2,I1:4} 19/25

20 How much batter is it 1/3? Ivan Tanasic (itanasic@gmail.com) Runtime on sparse data: 20/25

21 How much batter is it 2/3? Runtime on mixed data: Ivan Tanasic (itanasic@gmail.com)21/25

22 How much batter is it 3/3? Compactness: Ivan Tanasic (itanasic@gmail.com)22/25

23 Is it Original? A lot of methods try to improve Apriori ◦ Hashing ◦ Transaction reduction ◦ Partitioning ◦ Sampling TreeProjection uses similar structure, but it is still a different method Ivan Tanasic (itanasic@gmail.com)23/25

24 Importance over time Basic primitive (strong foundation for tall building) Performance gets very important as databases are getting huge Scalability also FP-Growth has both performance and scalability Ivan Tanasic (itanasic@gmail.com)24/25

25 Conclusion An important method for solving important DM tasks Fast Compact Scalable (db projection/tree on disk) Ivan Tanasic (itanasic@gmail.com)25/25

26 Mining Frequent Patterns Using FPGrowth Method Ivan Tanasić (itanasic@gmail.com) Department of Computer Engineering and Computer Science, School of Electrical Engineering, University of Belgrade

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