1. Data Mining Frequent-Pattern Tree Approach Towards ARM Lecture 11-12

2. 2 Is Apriori Fast Enough? — Performance Bottlenecks The core of the Apriori algorithm: –Use frequent (k – 1)-itemsets to generate candidate frequent k- itemsets –Use database scan and pattern matching to collect counts for the candidate itemsets The bottleneck of Apriori: candidate generation –Huge candidate sets: 10 4 frequent 1-itemset will generate 10 7 candidate 2-itemsets To discover a frequent pattern of size 100, e.g., {a 1, a 2, …, a 100 }, one needs to generate 2 100  10 30 candidates. –Multiple scans of database: Needs (n +1 ) scans, n is the length of the longest pattern

3. 3 Mining Frequent Patterns Without Candidate Generation Steps 1.Compress a large database into a compact, Frequent-Pattern tree (FP-tree) structure 1.highly condensed, but complete for frequent pattern mining 2.avoid costly database scans 2.Develop an efficient, FP-tree-based frequent pattern mining method 1.A divide-and-conquer methodology: decompose mining tasks into smaller ones 2.Avoid candidate generation: sub-database test only!

4. 4 FP-tree Construction Item frequency head f 4 c 4 a 3 b 3 m 3 p 3 TIDItems bought (ordered) frequent items 100{f, a, c, d, g, i, m, p}{f, c, a, m, p} 200{a, b, c, f, l, m, o}{f, c, a, b, m} 300 {b, f, h, j, o}{f, b} 400 {b, c, k, s, p}{c, b, p} 500 {a, f, c, e, l, p, m, n}{f, c, a, m, p} Steps: 1.Scan DB once, find frequent 1- itemset (single item pattern) 2.Order frequent items in frequency descending order 3.Scan DB again, construct FP-tree

5. 5 Steps Contd. (Example) –Scan of the first transaction leads to the construction of the first branch of the tree listing {} f:1 c:1 a:1 m:1 p:1 FP-tree Construction (contd.) (ordered) frequent items {f, c, a, m, p} {f, c, a, b, m} {f, b} {c, b, p} {f, c, a, m, p}

6. 6 {} f:2 c:2 a:2 b:1m:1 p:1m:1 FP-tree Construction (contd.) (ordered) frequent items {f, c, a, m, p} {f, c, a, b, m} {f, b} {c, b, p} {f, c, a, m, p} Steps Contd. (Example) –Scan of the first transaction leads to the construction of the first branch of the tree listing –Second transaction shares a common prefix with the existing path the count of each node along the prefix is incremented by 1 –Two new nodes are created and linked as children of (a:2) and (b:1) respec.

7. 7 Steps Contd. (Example) –Scan of the first transaction leads to the construction of the first branch of the tree listing –Second transaction shares a common prefix with the existing path the count of each node along the prefix is incremented by 1 –Two new nodes are created and linked as children of (a:2) and (b:1) respec. –Similarly for the third transaction {} f:3 b:1c:2 a:2 b:1m:1 p:1m:1 FP-tree Construction (contd.) (ordered) frequent items {f, c, a, m, p} {f, c, a, b, m} {f, b} {c, b, p} {f, c, a, m, p}

8. 8 Steps Contd. (Example) –Scan of the first transaction leads to the construction of the first branch of the tree listing –Second transaction shares a common prefix with the existing path the count of each node along the prefix is incremented by 1 –Two new nodes are created and linked as children of (a:2) and (b:1) respec. –Similarly for the third transaction –The scan of the fourth transaction leads to the construction of the second branch of the tree, (c:1), (b:1), (p:1). {} f:3c:1 b:1 p:1 b:1c:2 a:2 b:1m:1 p:1m:1 FP-tree Construction (contd.) (ordered) frequent items {f, c, a, m, p} {f, c, a, b, m} {f, b} {c, b, p} {f, c, a, m, p}

9. 9 Steps Contd. (Example) –Scan of the first transaction leads to the construction of the first branch of the tree listing –Second transaction shares a common prefix with the existing path the count of each node along the prefix is incremented by 1 –Two new nodes are created and linked as children of (a:2) and (b:1) respec. –Similarly for the third transaction –The scan of the fourth transaction leads to the construction of the second branch of the tree, (c:1), (b:1), (p:1). –For the last transaction, since its frequent item list is identical to the first one, the path is shared. {} f:4c:1 b:1 p:1 b:1c:3 a:3 b:1m:2 p:2m:1 FP-tree Construction (contd.) (ordered) frequent items {f, c, a, m, p} {f, c, a, b, m} {f, b} {c, b, p} {f, c, a, m, p}

10. 10 Create a Header table –Each entry in the frequent-item-header table consists of two fields, (1) item-name (2) head of node-link (a pointer pointing to the first node in the FP-tree carrying the item-name). FP-tree Construction (contd.) {} f:4c:1 b:1 p:1 b:1c:3 a:3 b:1m:2 p:2m:1 Header Table Item frequency head f4 c4 a3 b3 m3 p3

11. 11 Mining frequent patterns using FP-tree Mining frequent patterns out of FP-tree is based upon following Node-link property –For any frequent item a i, all the possible patterns containing only frequent items and a i can be obtained by following ai ’s node-links, starting from a i ’s head in the FP-tree header. Lets go through an example to understand the full implication of this property in the mining process.

12. 12 For node p, its immediate frequent pattern is (p:3), and it has two paths in the FP-tree: (f :4, c:3, a:3,m:2,p:2) and (c:1, b:1, p:1) These two prefix paths of p, “{( f cam:2), (cb:1)}”, form p’s conditional pattern base Now, we build an FP- tree on P’s conditional pattern base. Leads to an FP tree with one branch only i.e. C:3 hence the frequent patter n associated with P is just CP {} f:4c:1 b:1 p:1 b:1c:3 a:3 b:1m:2 p:2m:1 Header Table Item head f c a b m p Mining frequent patterns of p

13. 13 Mining frequent patterns of m Constructing an FP-tree on m, we derive m’s conditional FP-tree, f :3, c:3, a:3, a single frequent pattern path. This conditional FP-tree is then mined recursively. m-conditional pattern base: fca:2, fcab:1 {} f:3 c:3 a:3 m-conditional FP-tree All frequent patterns concerning m m, fm, cm, am, fcm, fam, cam, fcam   {} f:4c:1 b:1 p:1 b:1c:3 a:3 b:1m:2 p:2m:1 Header Table Item frequency head f4 c4 a3 b3 m3 p3

14. 14 Mining frequent patterns of m {} f:3 c:3 a:3 m-conditional FP-tree Cond. pattern base of “am”: (fc:3) {} f:3 c:3 am-conditional FP-tree Cond. pattern base of “cm”: (f:3) {} f:3 cm-conditional FP-tree Cond. pattern base of “cam”: (f:3) {} f:3 cam-conditional FP-tree

15. 15 Mining Frequent Patterns by Creating Conditional Pattern-Bases Empty f {(f:3)}|c{(f:3)}c {(f:3, c:3)}|a{(fc:3)}a Empty{(fca:1), (f:1), (c:1)}b {(f:3, c:3, a:3)}|m{(fca:2), (fcab:1)}m {(c:3)}|p{(fcam:2), (cb:1)}p Conditional FP-treeConditional pattern-base Item

16. 16 Single FP-tree Path Generation Suppose an FP-tree T has a single path P The complete set of frequent pattern of T can be generated by enumeration of all the combinations of the sub-paths of P {} f:3 c:3 a:3 m-conditional FP-tree All frequent patterns concerning m m, fm, cm, am, fcm, fam, cam, fcam 

17. 17 Why Is Frequent Pattern Growth Fast? Our performance study shows –FP-growth is an order of magnitude faster than Apriori, and is also faster than tree-projection Reasoning –No candidate generation, no candidate test –Use compact data structure –Eliminate repeated database scan –Basic operation is counting and FP-tree building

18. 18 FP-Growth vs. Apriori: Scalability With the Support Threshold Data set T25I20D10K #TransactionsItemsAverage Transaction Length 250,000100012

19. 19 null A:7 B:5 B:3 C:3 D:1 C:1 D:1 C:3 D:1 E:1 Pointers are used to assist frequent itemset generation D:1 E:1 Transaction Database Header table Frequent Itemset Using FP-Growth (Example)

20. 20 null A:7 B:5 B:3 C:3 D:1 C:1 D:1 C:3 D:1 E:1 D:1 E:1 Build conditional pattern base for E: P = {(A:1,C:1,D:1), (A:1,D:1), (B:1,C:1)} Recursively apply FP- growth on P E:1 D:1 FP Growth Algorithm: FP Tree Mining Frequent Itemset Using FP-Growth (Example)

21. 21 null A:2 B:1 C:1 D:1 E:1 Conditional Pattern base for E: P = {(A:1,C:1,D:1,E:1), (A:1,D:1,E:1), (B:1,C:1,E:1)} Count for E is 3: {E} is frequent itemset Recursively apply FP- growth on P ( Conditional tree for D within conditional tree for E) E:1 Conditional tree for E: FP Growth Algorithm: FP Tree Mining Frequent Itemset Using FP-Growth (Example)

22. 22 Conditional pattern base for D within conditional base for E: P = {(A:1,C:1,D:1), (A:1,D:1)} Count for D is 2: {D,E} is frequent itemset Recursively apply FP- growth on P (Conditional tree for C within conditional tree D within conditional tree for E) Conditional tree for D within conditional tree for E: null A:2 C:1 D:1 FP Growth Algorithm: FP Tree Mining Frequent Itemset Using FP-Growth (Example)

23. 23 Conditional pattern base for C within D within E: P = {(A:1,C:1)} Count for C is 1: {C,D,E} is NOT frequent itemset Recursively apply FP- growth on P (Conditional tree for A within conditional tree D within conditional tree for E) Conditional tree for C within D within E: null A:1 C:1 FP Growth Algorithm: FP Tree Mining Frequent Itemset Using FP-Growth (Example)

24. 24 Count for A is 2: {A,D,E} is frequent itemset Next step: Construct conditional tree C within conditional tree E Conditional tree for A within D within E: null A:2 FP Growth Algorithm: FP Tree Mining Frequent Itemset Using FP-Growth (Example)

25. 25 null A:2 B:1 C:1 D:1 E:1 Recursively apply FP- growth on P ( Conditional tree for C within conditional tree for E) E:1 Conditional tree for E: FP Growth Algorithm: FP Tree Mining Frequent Itemset Using FP-Growth (Example)

26. 26 null A:1 B:1 C:1 E:1 FP Growth Algorithm: FP Tree Mining Conditional pattern base for C within conditional base for E: P = {(B:1,C:1), (A:1,C:1) } Count for C is 2: {C,E} is frequent itemset Recursively apply FP- growth on P (Conditional tree for B within conditional tree C within conditional tree for E) Conditional tree for C within conditional tree for E: Frequent Itemset Using FP-Growth (Example)

27. 27 null A:7 B:5 B:3 C:3 D:1 C:1 D:1 C:3 D:1 E:1 D:1 E:1 Transaction Database Header table FP Growth Algorithm: FP Tree Mining Frequent Itemset Using FP-Growth (Example)