1. Mining Multiple-level Association Rules in Large Databases Authors : Jiawei Han Simon Fraser University, British Columbia. Yongjian Fu University of Missouri-Rolla, Missouri. Presented by Michael Johnson IEEE Transactions on Knowledge and Data Engineering, 1999 1

2. Outline 1. What is MLAR? ● Concepts ● Motivation 2. A Method For Mining M-L Association Rules ● Problems/Solutions ● Definitions ● Algorithm Example ● Interestingness ● Optimizations 3. Conclusions/Future Work 4. Exam Questions 2

3. Outline 1. What is MLAR? ● Concepts ● Motivation 2. A Method For Mining M-L Association Rules ● Problems/Solutions ● Definitions ● Algorithm Example ● Interestingness ● Optimizations 3. Conclusions/Future Work 4. Exam Questions 3

4. What is MLDM? What's the difference between the following rules: Rule A →70% of customers who bought diapers also bought beer Rule B →45% of customers who bought cloth diapers also bought dark beer Rule C →35% of customers who bought Pampers also bought Samuel Adams beer. 4

5. Rule A applies at a generic higher level of abstraction (product) Rule B applies at a more specific level of abstraction (category) Rule C applies at the lowest level of abstraction (brand). This process is called drilling down. 5 What is MLDM?

6. What Do We Gain? Concrete rules allow for  More targeted marketing  New marketing strategies  More concrete relationships 6

7. Hierarchy Types Generalization/Specialization (is a relationships) Is a With Multiple Inheritance Whole-Part hierarchies (is-part-of; has-part) 7

8. Is A Relationship 2-Wheels MotorcycleSUV Vehicle 4-Wheels SedanBicycle 8 Generalization to Specialization

9. Is A With Multiple Inheritance Recreational SnowmobileBicycle Vehicle Commuting Car 9

10. Whole-Part Hierarchies Hard Drive PlatterCPU Computer Motherboard RAM RW Head 10

11. Outline 1. What is MLAR? ● Concepts ● Motivation 2. A Method For Mining M-L Association Rules ● Problems/Solutions ● Definitions ● Algorithm Example ● Interestingness 3. Conclusions/Future Work 4. Exam Questions 11

12. MLAR: Main Goal As usual we are trying to develop a method to extract non-trivial, interesting, and strong rules from our transactional database. A method which: Avoids trivial rules (Milk→Bread) Common sense Avoids coincidental rules (Toy→Milk) Low support 12

13. What Do We Need? 1. Data Representation At Different Levels Of Abstraction ● Explicitly stored in databases ● Provided by experts or users ● Generated via clustering (OLAP) 2. Efficient Methods for ML Rule Mining (focus of this paper) 13

14. Possible Methods Apply single-level Apriori Algorithm to each of the multiple levels under the same miniconf and minsup. Potential Problems? Higher Levels of abstraction will naturally have higher support, support decreases as we drill down What is the optimal minsup for all levels? Too high a minsup → too few itemsets for lower levels Too low a minsup → too many uninteresting rules 14

15. Possible Solutions Adapt minsup for each level Adapt minconf for each level Do both This paper studies a progressive deepening method developed by extension of the Apriori Algorithm, focused on minsup 15

16. Assumption If an item is non-frequent at one level, its descendants no longer figure in further analysis. Explore only descendants of frequent items as we drill down Are there problems that arise from making these assumptions? 16

17. May eliminate possible interesting rules for itemsets at one level whose ancestors are not frequent at higher levels.  If so, can be addressed by 2 workarounds level passage threshold 2 minsup values at higher levels – one for filtering infrequent items, the other for passing down frequent items to lower levels; latter called level passage threshold (lph) sub frequent The lph may be adjusted by user to allow descendants of sub frequent items 17 Problems

18. Differences From Previous Research Other studies use same minsup across different levels of the hierarchy This study…. Uses different minsup values at different levels of the hierarchy Analyzes different optimization techniques Studies the use of interestingness measures 18

19. Requirements Transactional database must contain: 1. Item dataset containing item description: {, } 2. A transaction dataset T containing set of transactions.. {Tid*,{Ap…Aq}} *Tid is a transaction identifier (key) 19

20. Algorithm Flow 20 At Level 1: Generate frequent itemsets Get table filtered for frequent itemsets T[2] At subsequent levels: Generate candidate subsets using Apriori Calculate support for generated candidates Union 'passing' subsets with existing rule set Repeat until no additional rules are generated, or desired level is reached

21. Outline 1. What is MLAR? ● Concepts ● Motivation 2. A Method For Mining M-L Association Rules ● Problems/Solutions ● Definitions ● Algorithm Example ● Interestingness ● Optimizations 3. Conclusions/Future Work 4. Exam Questions 21

22. Definitions 22 A pattern or an itemset A is one item Ai or a set of conjunctive items Ai Λ …. Λ Aj The support of a pattern is the number of transactions that contain A vs. the total number of transactions  σ(A|S) The confidence of a rule A → B in S is given by: φ(A→B) = σ(AUB)/σ(A) (i.e. conditional probability) Specify 2 thresholds: minsup(σ’) and miniconf (φ’); different values at different levels

23. Definitions A pattern A is frequent in set S at if: the support of A is no less than the corresponding minimum support threshold σ’ A rule A → B is strong for a set S, if: a. each ancestor of every item in A and B is frequent at its corresponding level b. A Λ B is frequent at the current level and c. φ(A→B)≥ φ’ (miniconf criteria) This ensures that the patterns examined at the lower levels arise from itemsets that have a high support at higher levels 23

24. Outline 1. What is MLAR? ● Concepts ● Motivation 2. A Method For Mining M-L Association Rules ● Problems/Solutions ● Definitions ● Algorithm Example ● Interestingness 3. Conclusions/Future Work 4. Exam Questions 24

25. food milk 2% chocolate bread whitewheat Dairyland Foremost Old MillsWonder Generalized ID System: 2% Foremost Milk Coded as GID:112 (1 st item in Level 1, 1 st item in level 2, 2 nd item in level 3) 25 Level 1: Level 2: Level 3:... Example: Taxonomy

26. Trans-idBar_code_set 351428{17325, 92108, ….} 653234{23423, 56432,…} Bar_codeGIDCategoryBrandContentSizePrice 17325112milkForemost2%1 Gal$3.31 ….. ……….. Table 1: A sales-transaction Table Table 2: A sales_item Description Relation 26 Example: Dataset

27. TIDItems T1{111, 121, 211, 221} T2{111, 211, 222, 323} T3{112, 122, 221, 411} T4{111, 121} T5{111, 122, 211, 221, 413} T6{211, 323, 524} T7{323, 411, 524, 713} 27 Example: Preprocessing Join sales_transaction table to sales_item table to produce encoded transaction table T[1]:

28. T[1] Find Level-1 frequent itemsets Minsup = 4 Level-1 Frequent-1 Itemsets L[1,1] 28 Example: Step 1 TIDItems T1{111, 121, 211, 221} T2{111, 211, 222, 323} T3{112, 122, 221, 411} T4{111, 121} T5{111, 122, 211, 221, 413} T6{211, 323, 524} T7{323, 411, 524, 713} ItemsetSupport {1**}5 {2**}5 Level-1 Frequent 2-Itemsets L[1,2] ItemsetSupport {1**,2**}4

29. T[1] Create T[2] by filtering T[1] w/ L[1,1] 29 Example: Step 2 TIDItems T1{111, 121, 211, 221} T2{111, 211, 222, 323} T3{112, 122, 221, 411} T4{111, 121} T5{111, 122, 211, 221, 413} T6{211, 323, 524} T7{323, 411, 524, 713} ItemsetSupport {1**}5 {2**}5 Filtered T[2] TIDItems T1{111, 121, 211, 221} T2{111, 211, 222} T3{112, 122, 221} T4{111, 121} T5{111, 122, 211, 221} T6{211}

30. 30 Example: Step 3 Filtered T[2] TIDItems T1{111, 121, 211, 221} T2{111, 211, 222} T3{112, 122, 221} T4{111, 121} T5{111, 122, 211, 221} T6{211} ItemsetSupport {11*, 12*, 22*}3 {11*, 21*, 22*}3 L[2,1] ItemsetSupport {11*, 12*}4 {11*, 21*}3 {11*,22*}4 {12*, 22*}3 {21*, 22*}3 L[2,2] ItemsetSupport {11*}5 {12*}4 {21*}4 {22*}4 L[2,2] L[2,3] Find Level-2 Frequent Itemsets Minsup = 3

31. 31 Example: Step 4 Filtered T[2] TIDItems T1{111, 121, 211, 221} T2{111, 211, 222} T3{112, 122, 221} T4{111, 121} T5{111, 122, 211, 221} T6{211} ItemsetSupport {111, 211*}3 L[3,1] ItemsetSupport {111}4 {211}4 {221}3 L[3,2] Find Level-3 Frequent Itemsets Minsup = 3 Stop: Lowest Level Reached

32. Outline 1. What is MLAR? ● Concepts ● Motivation 2. A Method For Mining M-L Association Rules ● Problems/Solutions ● Definitions ● Algorithm Example ● Interestingness ● Optimizations 3. Conclusions/Future Work 4. Exam Questions 32

33. 33 Are All Of The Strong Rules Interesting? MLDM creates unique challenges for rule pruning The paper defines two filters for interesting rules: 1. Removal of redundant rules 2. Removal of unnecessary rules

34. Redundant Rules 34 Consider a strong rule at Level 1: Milk→Bread food milk 2% chocolate bread whitewheat Dairyland Foremost Old MillsWonder... This rule is likely to have descendent rules which may or may not contain additional information, even if they met our minconf and minsup criteria at that level: 2% Milk→Wheat Bread, 2% Milk→White Bread, Chocolate Milk→Wheat Bread We need a way to distinguish between rules that add information and those that are redundant

35. 35 A rule is redundant if the confidence for a rule falls in a certain range and the items in the rule are descendents of a different rule. Redundant Rules

36. 36 Applying Redundant Rule reduction eliminates 40-70% of discovered Strong Rules Redundant Rules

37. Unnecessary Rules 37 Consider the following rules: R: Milk→Bread (minsup = 80%) R': Milk, Butter → Bread (minsup = 80%) How much additional information do we gain from the R'? MLDM can produced very complex rules that meet our minsup and minconf criteria, but do contain much unique/useful information. We need a way to distinguish between rules that add information and those that are Unnecessary

38. 38 A rule R is unnecessary if there is a simpler rule R' and φ(R) is within a given range of φ(R') Unnecessary Rule

39. Outline 1. What is MLAR? ● Concepts ● Motivation 2. A Method For Mining M-L Association Rules ● Problems/Solutions ● Definitions ● Algorithm Example ● Interestingness ● Optimizations 3. Conclusions/Future Work 4. Exam Questions 39

40. 40 Hardware Setup Hardware: Sun Microsystems SPARCstation 20 1. 32MB RAM 2. 100Mhz Clock 3. CLI

41. 41 Authors proposed 3 iterations of the original algorithm 1)ML_T1LA ● Use only one encoded table T[1] 2)ML_TML1 ● Generate T[1], T[2], … T[n+1] 3)ML_T2LA ● Uses T[2], but calculates down level support with a single scan Algorithm Optimizations

42. 42 Instead of generating T[2] from T[1], ML_T1LA algorithm generates support for all levels of hierarchy in a single scan from T[1] Pros: Avoids generation of new transaction table Limits number of scans to the size of the largest transaction Cons: Scanning T[1] requires scanning all items, even infrequent ones Performance may suffer for DB w/ many infrequent itemsets Large memory required (32MB RAM = page swapping!!!) ML_T1LA

43. 43 Instead of using only T[2] for rule mining, ML_TML1 algorithm generates a table for each level, using L[i,1] to filter T[i] and create T[i+1] Pros: Saves significant processing time if only a small portion of the data is frequent at each level ● Allows for creation of T[i] and L[i,1] in parallel Cons: May not be efficient if only a small number of items is filtered at each level ML_TML1

44. 44 Like the base algorithm, ML_T2LA creates T[2] table from the frequent itemsets in T[1]. However, it allows for parallel creation of L[i,k]. Pros: Saves time by limiting the number of scans Cons: May not be efficient if only a small number of items is filtered at each level ML_T2LA

45. 45 While the figures show that T2LA is best for most of the time, the authors preferred ML_T1LA Experimental Results

46. Outline 1. What is MLAR? ● Concepts ● Motivation 2. A Method For Mining M-L Association Rules ● Problems/Solutions ● Definitions ● Algorithm Example ● Interestingness ● Optimizations 3. Conclusions/Future Work 4. Exam Questions 46

47. Conclusions This paper demonstrated: Extending association rules from single-level to multiple- level. A top-down progressive deepening technique for mining multiple-level association rules. Filtering of uninteresting association rules Performance optimization techniques (not covered) 47

48. Future Work Develop efficient algorithms for mining multiple-level sequential patterns Cross-level associations Improve interestingness of rules 48

49. Outline 1. What is MLAR? ● Concepts ● Motivation 2. A Method For Mining M-L Association Rules ● Problems/Solutions ● Definitions ● Algorithm Example ● Interestingness ● Optimizations 3. Conclusions/Future Work 4. Exam Questions 49

50. Exam Question 1 Q. What is a major drawback to multiple-level data mining using the same minsup at all levels of a concept hierarchy? A. Large support exists at higher levels of the hierarchy; smaller support at lower levels. In order to insure that sufficiently strong association rules are generated at the lower levels, we must reduce the support at higher levels which, in turn, could result in generation of many uninteresting rules at higher levels. Thus we are faced with the problem of determining which is the optimal minsup at all levels 50

51. Exam Question 2 Q. Give an example of a multiple level association rule A. High level: 80% of people who buy cereal also buy milk Low Level: 25% of people who buy Cheerios cereal buy Hood 2% Milk 51

52. Exam Question 3 Q. There were 3 examples of hierarchy types in multiple level rule mining. Pick one and draw an example 52 2-Wheels Motorcycle SUV Vehicle 4-Wheels Sedan Bike Recreational SnowmobileBicycle Vehicle Commuting Car Hard Drive PlatterCPU Computer Motherboard RAM RW Head Is-A Multiple Inheritance Whole-Part