A Lazy Approach to Associative Classification

1. Elena Baralis, Silvia Chiusano, Paolo Garza Dipartimento di Automatica e Informatica, Politecnico di Torino, ITALY A Lazy Approach to Associative Classification IEEE Transactions on Knowledge and Data Engineering ’08 Feb.

2. Outline • Introduction • Compact Rule Set Representation • Classification model generation • L3Gen Mining Algorithm • Lazy Pruning (L3) • Experiments • Conclusion

3. Introduction • Excessive rule sets size • A huge number of classification rules • Over-pruning • Discarding useful and low-quality rules • The Goal • To minimize the size of the high-quality rules

4. Compact Rule Set Representation • Compact form of a classification rule set • Concise and complete representation • Regenerated from the compact form Class Generator Closed itemset

5. Compact Rule Set Representation • Correlated items (Macroitem) • The items are contained in the same transactions

6. Compact Rule Set Representation • Closed itemsets • The maximal set of items common to a set of transactions • γ (X) = X {a, b, c, d}

7. Compact Rule Set Representation • Generator itemsets • G is a generator itemset of a closed itemset X • γ (G) = X, ∃G ∈ {G1, G2, …, Gn} Generators: {a,b}, {a,c}, {a}, {b}, {c}, {d}, {a,b}, {a,c}, {a,d}, {b,c}, {b,d}, {c,d}, {a,b,c}, {a,b,d}, {a,c,d}, {b,c,d}, {a,b,c,d} Closed itemsets: Support >= 16.67% Confidence >= 50%

8. Classification model generation • Classification rule extraction • Using compact representation to perform rule extraction with low support thresholds • Classification rule pruning • Providing high-quality rules for classification

9. L3Gen Mining Algorithm • Recursive projection of macrodata set • Macroitem of minimum supportis considered first • : the set of macroitems

10. L3Gen Mining Algorithm • Set updating • The macroitems that included in all transactions are removed from and add to

11. L3Gen Mining Algorithm • Compact rule mining • Compact rules satisfy the support, confidence • Each rule is labeled by a class label

12. L3Gen Mining Algorithm • Data set project • The used macroitems is removed from and add to

13. Lazy Pruning • The three subsets of rules • Used rules • Correctly classified at least one training data instance • Spare rules • That have not been used during the training phase • May become useful to classify data • Harmful rules • That only wrongly classify training data • Pruning target

14. Lazy Pruning • Rule rank • confidence(ri) > confidence (rj) • support(ri) > support (rj) • length(ri) > length(rj) • The number of items • lex(ri) > lex(rj) • The position of r in the lexicographic order on items

15. Lazy Pruning (L3) Training data set • Closed itemsets of Compact rules Rules

16. Lazy Pruning (L3) Training data set Rules

17. Lazy Pruning (L3) Training data set Rules

18. Lazy Pruning (L3) Training data set Rules

19. Lazy Pruning (L3) Training data set Rules Harmful rules

20. Lazy Pruning (L3) Training data set Rules

21. Lazy Pruning (L3) Training data set Rules

22. Lazy Pruning (L3) • Lazy Pruning (L3) Rules Level-1 Used rules Level-2 Spare rules + Level-1 & Level-2 Compact rule itemsets

23. Experiments

24. Experiments • L3 versus the Other Classifiers • higher than/equal to/lower than

25. Conclusion • The compact form • It allows representing very large rule sets • The lazy pruning technique discards only harmful rules • Two Level classification • Level-1 includes few high-quality rules • Level-2 provides Level-1 did not classify rules