1. USpan: An Efficient Algorithm for Mining High Utility Sequential Patterns Authors: Junfu Yin, Zhigang Zheng, Longbing Cao In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012. p. 660-668. Presenter: 0356069 江怡蕙 0356027 薛筑軒

2. Outline Introduction Related work Problem Statement USpan algorithm Experiment Conclusions & Discussions 2

3. Outline Introduction Background Definition Challenges Related work Problem Statement USpan algorithm Experiment Conclusions & Discussions 3

4. Introduction Sequential pattern mining has proven to be very essential for handling order-based critical business problems. EX: structures and functions of molecular or DNA sequences 4

5. Background The selection of interesting sequences is generally based on the frequency/support framework: sequences of high frequency are treated as significant. Under this framework, the downward closure property (also known as Apriori property) plays a fundamental role. 5

6. Utility Internal utility = quantity ; External utility = quality High utility pattern mining Minimum utility The utility of in sequence 2 is {(6 × 1 + 1 × 2), (6 × 1 + 2 × 2)} = {8, 10} 6 Definition

7. The concept of sequence utility by considering the quality and quantity associated with each item in a sequence, and define the problem of mining high utility sequential patterns; A complete lexicographic quantitative sequence tree (LQS-tree) to construct utility-based sequences; two concatenation mechanisms I-Concatenation and S-Concatenation generate newly concatenated sequences; 7 Definition

8. Two pruning methods, width and depth pruning, substantially reduce the search space in the LQS- tree; USpan traverses LQS-tree and outputs all the high utility sequential patterns. 8

9. Outline Introduction Related work Utility Itemset/Pattern Mining Utility-based Sequential Pattern Mining Problem Statement USpan algorithm Experiment Conclusions & Discussions 9

10. Mining high utility itemsets is much more challenging than discovering frequent itemsets, because the fundamental downward closure property in frequent itemset mining does not hold in utility itemsets. The addition of ordering information in sequences makes it fundamentally different and much more challenging than mining utility itemsets 10 Utility Itemset/Pattern Mining

11. Utility-based Sequential Pattern Mining Mining frequent sequences  many patterns being mined; Patterns with frequencies lower than minimum support are filtered 11

12. Outline Introduction Related work Problem Statement USpan algorithm Experiment Conclusions & Discussions 12

13. Sequence Utility Framework I = {i1, i2,..., in}  a set of distinct items Each item ik ∈ I(1<= k<=n) is associated with a quality (or external utility), denoted as p(ik) A quantitative item, or q-item, is an ordered pair (i, q), where i ∈ I represents an item and q is a positive number representing the quantity or internal utility 13

14. A quantitative itemset, or q-itemset, consists of more than one q-item, which is denoted and defined as l = [(ij1, q1)(ij2, q2)...(ijn, qn )] A quantitative sequence, or q-sequence, is an ordered list of qitemsets, which is denoted and defined as s = A q-sequence database S consists of sets of tuples 14 Sequence Utility Framework

15. Sequence Utility Framework- Definitions 15 EX: (a, 4), [(a, 4)(e, 2)] and [(a, 4)(b, 1)(e, 2)] ⊆ [(a, 4)(b, 1)(e, 2)] But [(a, 2)(e, 2)] or [(a, 4)(c, 1)] not contained in [(a, 4)(b, 1)(e, 2)],,

16. Sequence Utility Framework- Definitions 16 is a 4-q-sequence with size 3. is a 2-sequence with size 2.

17. 17 Sequence Utility Framework- Definitions

18. 18 Sequence Utility Framework- Definitions

19. 19 Sequence Utility Framework- Definitions t = t’s utility in the s4 sequence in Table 2 is v(t, s4) = {u( ), u(<(e, 2) (a, 4)>)} = {16, 10}. t’s utility in S is v(t) = {u(t, s2), u(t, s4),u(t, s5)} = {{8, 10}, {16, 10}, {15, 7}}

20. 20 High Utility Sequential Pattern Mining

21. Definition 10. (High Utility Sequential Pattern) Because a sequence may have multiple utility values in the q-sequence context, we choose the maximum utility as the sequence’s utility. The maximum utility of a sequence t is denoted and defined as umax(t): Sequence t is a high utility sequential pattern if and only if ξ  user-specified minimum utility 21 The utility of sequence ea is umax( ) =10 + 16 + 15 = 41. If the minimum utility is ξ = 40, then sequence s = is a high utility sequential pattern since umax(s) = 41 ≥ ξ

22. Outline Introduction Related work Problem Statement USpan algorithm Lexicographic Q-Sequence Tree Concatenations Width Pruning Depth Pruning USpan Algorithm Experiment Conclusions & Discussions 22

23. USpan Algorithm USpan is composed of a lexicographic q-sequence tree two concatenation mechanisms two pruning strategies 23

24. Lexicographic Q-Sequence Tree Adapt the concept of the Lexicographic Sequence Tree Suppose we have a k-sequence t, we call the operation of appending a new item to the end of t to form (k+1)- sequence concatenation. If the size of t does not change, we call the operation I-Concatenation. Otherwise, if the size increases by one, we call it S-Concatenation ’s I-Concatenate and S-Concatenate with b result in and, respectively. 24

25. Lexicographic Q-Sequence Tree Assume two k-sequences ta and tb are concatenated from sequence t, then ta < tb if i) ta is I-Concatenated from t, and tb is S-Concatenated from t, ii) both ta and tb are I-Concatenated or S-Concatenated from t, but the concatenated item in ta is alphabetically smaller than that of tb.,, and 25

26. Lexicographic Q-Sequence Tree Definition 11. (Lexicographic Q-sequence Tree) An lexicographic q-sequence tree (LQS-Tree) T is a tree structure satisfying the following rules: Each node in T is a sequence along with the utility of the sequence, while the root is empty Any node’s child is either an I-Concatenated or S- concatenated sequence node of the node itself All the children of any node in T are listed in an incremental and alphabetical order 26

27. v(ea) = {{8, 10}, {16, 10}, {15, 7}} and umax(ea) = 41. “Can any ’s child’s maximum utility be calculated by simply adding the highest utility of the q-items after to umax(ea)?” 27 Lexicographic Q-Sequence Tree ---------------- no

28. Depth-first search How can we generate the node’s children’s utilities by concatenating the corresponding items? How can we avoid checking unpromising children? When should USpan stop the search of deeper nodes? 28 Lexicographic Q-Sequence Tree Concatenations Width pruning Depth pruning

29. Concatenations Utility matrix (utility, remaining utility) 29

30. Concatenations Utility matrix (utility, remaining utility) 30

31. Concatenations Utility matrix (utility, remaining utility) 31

32. Concatenations: I-Concatenation 32

33. Concatenations: S-Concatenation 33

34. Concatenations 34

35. Concatenations 35

36. Width Pruning 36

37. Width Pruning 37

38. Depth Pruning 38

39. Depth Pruning 39

40. USpan Algorithm // includes depth pruning strategy // width pruning strategy // generate candidates // deal with I-Concatenation // deal with S-Concatenation 40

41. Outline Introduction Related work Problem Statement USpan algorithm Experiment Settings Results Conclusions & Discussions 41

42. Experimental Settings Data Sets DS1: C10 T2.5 S4 I2.5 DB10k N1k DS2: C8 T2.5 S6 I2.5 DB10k N10k The average number of elements in a sequence is 10 (8). The average number of items in an element is 2.5 (2.5). The average length of a maximal pattern consists of 4 (6) elements and each element is composed of 2.5 (2.5) items average. The data set contains 10k (10k) sequences. The number of items is 1k (10k). 42

43. Experimental Settings Data Sets DS3: online shopping transactions There are 811 distinct products, 350,241 transactions and 59,477 customers. The average number of elements in a sequence is 5. The max length of a customer’s sequence is 82. The most popular product has been ordered 2176 times. DS4: mobile communication transactions The dataset is a 100,000 mobile-call history. There are 67,420 customers in the dataset. The maximum length of a sequence is 152. 43

44. Experimental Results – Execution Time & (#Patterns) 44

45. Experimental Results – Execution Time & (#Patterns) 45

46. Experimental Results – Distribution in Terms of Length 46

47. Experimental Results – Distribution in Terms of Length 47

48. Experimental Results – Pruning 48

49. Experimental Results – Scalability 49

50. Experimental Results – Utility vs Frequent 50

51. Outline Introduction Related work Problem Statement USpan algorithm Experiment Conclusions & Discussions 51

52. Conclusions Provide a systematic statement of a generic framework for high utility sequential pattern mining. Propose an efficient algorithm, Uspan I-Concatenation, S-Concatenation Width pruning, depth pruning USpan can efficiently identify high utility sequences in large-scale data with low minimum utility. 52

53. Discussions Strongest part of this paper USpan grows tree by DFS and needs not to store the whole LQS-Tree in memory. Two pruning strategies are proposed and work well in their experiments. Only need to calculate the tables once at beginning. Weak points of this paper Each sequence needs a table to store it values and all the tables are stored in memory. Each single tree node contains much information. 53

54. Discussions Possible improvement Design algorithms for even bigger datasets and better pruning strategies. Shrink the number of tables or shrink the number of elements in a table. Possible extension The metric of “utility” Items with positive and negative unit profits Time constraints (as in GSP) Possible Application Business decision-making Analysis of game records of experts But need to specify “item” and “utility” first 54

55. END & Thanks for your attention