1. 1 Top Down FP-Growth for Association Rule Mining By Ke Wang

2. 2 Introduction Classically, for rule A  B : –support: computed by count( AB ) frequent --- if pass minimum support threshold –confidence: computed by count( AB ) / count(A ) confident – if pass minimum confidence threshold How to mine association rules? –find all frequent patterns –generate rules from the frequent patterns

3. 3 Introduction Limitations of current research –use uniform minimum support threshold –only use support as pruning measure Our contribution –improve efficiency –adopt multiple minimum supports –introduce confidence pruning

4. 4 Related work -- Frequent pattern mining Apriori algorithm –method: use anti-monotone property of support to do pruning, i.e. if length k pattern is infrequent, its length k+1 super-pattern can never be frequent FP-growth algorithm--better than Apriori –method: build FP-tree to store database mine FP-tree in bottom-up order

5. 5 Related work -- Association rule mining Fast algorithms trying to guarantee completeness of frequent patterns Parallel algorithms & association rule based query languages Various association rule mining problems –multi-level multi-dimension rule –constraints on specific item

6. 6 TD-FP-Growth for frequent pattern mining Similar tree structure as FP-growth –Compressed tree to store the database –nodes on each path of the tree are globally ordered Different mining method VS.FP-growth –FP-growth: bottom-up tree mining –TD-FP-Growth : top-down tree mining

7. 7 TD-FP-Growth for frequent pattern mining b: 2 root b: 1c: 1 a: 3 e: 1 c: 1e: 1 c: 1 e: 1 b, e a, b, c, e b, c, e a, c, d a minsup = 2 Entry valuecountside-link abceabce 33333333 Construct a FP-tree:

8. 8 b, e a, b, c, e b, c, e a, c, d a minsup = 2 itemHead of node-link abceabce TD-FP-Growth for frequent pattern mining FP-growth: bottom-up mining b: 2 root b: 1c: 1 a: 3 e: 1 c: 1e: 1 c: 1 e: 1 (b: 1) (b: 1, c: 1) (a: 1, b: 1, c: 1) e’s conditional pattern base Mining order: e, c, b, a

9. 9 TD-FP-Growth for frequent pattern mining FP-growth: bottom-up mining (b: 1) (b: 1, c: 1) (a: 1, b: 1, c: 1) root b: 3 c: 2 itemHead of node-link bcbc  drawback! both e’s conditional pattern base and conditional FP-tree are stored in memory mine e’s conditional FP-tree recursively conditional pattern bases and FP-trees are built for all other items and their super-patterns

10. 10 TD-FP-Growth for frequent pattern mining TD-FP-Growth : adopt top-down mining strategy –motivation: avoid building extra databases and sub-trees as FP-growth does –method: process nodes on the upper level before those on the lower level –result: any modification happened on the upper level nodes would not affect the lower level nodes See example 

11. 11 TD-FP-Growth for frequent pattern mining b, e a, b, c, e b, c, e a, c, d a minsup = 2 CT-tree and header table H Entry valuecountside-link abceabce 33333333 b: 2 root b: 1c: 1 a: 3 e: 1 c: 1e: 1 c: 1 e: 1 Mining order: a, b, c, e

12. 12 CT-tree for frequent pattern mining b, e a, b, c, e b, c, e a, c, d a minsup = 2 a: 2 b: 1 CT-tree and header table H b: 2 root b: 1c: 1 a: 3 e: 1 c: 1e: 1 c: 1 e: 1 sub-header-table H_c Entry valuecountside-link abab 2222 Entry valuecountside-link abceabce 33333333

13. 13 CT-tree for frequent pattern mining Completeness –for entry i in H, we mine all the frequent patterns that end up with item i, no more and no less Complete set of frequent patterns: {a } {b } {c }, {b, c }, {a, c } {e }, {b, e }, {c, e }, {b, c, e }

14. 14 TD-FP-Growth for frequent pattern mining Comparing to FP-growth, TD-FP-Growth is: –Space saving: only one tree and a few header tables no extra databases and sub-trees –Time saving: does not build extra databases and sub-trees walk up path only once to update count information for nodes on the tree and build sub- header-tables.

15. 15 TD-FP-Growth for association rule mining Assumptions: –There is a class-attribute in the database –Items in the class-attribute called class-items, others are non-class-items –Each transaction is associated a class-item –Only class-item appears in the right-hand of the rule Transaction ID non-class- attribute class-attribute 1a, b…C1C1 2d…C2C2 3e, d, f…C3C3 ……… example rule: a, b  C i

16. 16 TD-FP-Growth for association rule mining-- multi mini support Why? –Use uniform minimum support, computation of count considers only number of appearance –Uniform minimum support is unfair to items that appears less but worth more. Eg. responder vs. non-responder How? –Use different support threshold for different class

17. 17 TD-FP-Growth for association rule mining -- multi mini support multiple VS. uniform –C 1 : 4, C 2 : 2 –rules with relative minsup = 50% proportional to each class -- multiplier in performance uniform minimum support: absolute minsup = 1; –11 nodes tree, 23 rules multiple minimum supports: absolute minsup 1 = 2; absolute minsup 2 = 1; –7 nodes tree, 9 rules –more effective and space-saving –time-saving --- show in performance c, f, C 1 b, e, C 2 b, e, f, C 1 a, c, f, C 1 c, e, C 2 b, c, d, C 1

18. 18 TD-FP-Growth for association rule mining --conf pruning Motivation –make use of the other constraint of association rule: confidence, to speed up mining Method –confidence is not anti-monotone –introduce: acting constraint of confidence, which is anti-monotone –push it inside the mining process

19. 19 TD-FP-Growth for association rule mining --conf pruning conf(A  B) = count(AB) / count(A) >= minconf  count(AB) >= count(A) * minconf  count(AB) >= minsup * minconf (anti-monotone & weaker) --- the acting constraint of confidence for the original confidence constraint of rule A  B support of rule is computed by: count(A) count(AB): class-count of itemset A related to class B

20. 20 TD-FP-Growth for association rule mining --conf pruning c, f, C 1 b, e, C 2 b, e, f, C 1 a, c, f, C 1 a, c, d, C 2 minsup = 2 minconf= 60% Header table H: count(i) = count(i, C 1 ) + count(i, C 2 ) root b: 2 e: 2 …… … count(e) >= minsup; However, both count(e, C 1 ) & count(e, C 2 ) < minsup * minconf;  terminate mining for e! sub-header-table H_e If no confidence pruning  Entry value i count (i) count(i,Ci)count(i,C 2 ) side- link b211 Entry value i count (i) count(i,C 1 ) count(i,C 2 ) side- link abcefabcef 2232322323 1121311213 1111011110 ……………………

21. 21 Performance Choose several data sets from UC_Irvine Machine Learning Database Repository: h ttp://www.ics.uci.edu/~mlearn/MLRepository.html. name of dataset # of transactions # of items in each transaction class distribution # of distinct items Dna-train200061 23.2%, 24.25%, 52.55% 240 Connect-4 6755743 9.55%, 24.62%, 65.83% 126 Forest58101213 0.47%, 1.63%, 2.99%, 3.53%, 6.15%, 36.36%, 48.76% 15916

22. 22 Performance—frequent pattern

23. 23 Performance — mine rules with multiple minimum supports relative minsup, proportional to each class FP-growth is only for frequent pattern mining

24. 24 Performance — mine rules with confidence pruning

25. 25 Conclusions and future work Conclusions of TD-FP-Growth algorithm –more efficient in finding both frequent patterns and association rules –more effective in mining rules by using multiple minimum supports –Introduce a new pruning method: confidence pruning, and push it inside the mining process; thus further speed up mining

26. 26 Conclusions and future work Future work –Explore other constraint-based association rule mining method –Mine association rules with item concept hierarchy –Apply TD-FP-Growth to applications based on association rule mining Clustering Classification

27. 27 Reference (1) R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. Proc. 1993 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD’93), pages 207-216, Washington, D.C., May 1993. (2) U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (eds.). Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996. (3) H. Toivonen. Sampling large databases for association rules. Proc. 1996 Int. Conf. Very Large Data Bases (VLDB’96), pages 134-145, Bombay, India, September 1996. (4) R. Agrawal and S. Srikant. Mining sequential patterns. Proc. 1995 Int. Conf. Data Engineering (ICDE’95), pages 3-14, Taipei, Taiwan, March 1995. (5) J. Han, J. Pei and Y. Yin. Mining Frequent Patterns without Candidate Generation. Proc. 2000 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD’00), pages 1-12, Dallas, TX, May 2000. (6) J. Han, J. Pei, G. Dong, and K. Wang. Efficient Computation of Iceberg Cubes with Complex Measures. Proc. 2001 ACM-SIGMOD Int. Conf., Santa Barbara, CA, May 2001. And more!