The document discusses pattern mining techniques for extracting insights from log data, focusing on algorithms like Apriori and FP-Growth. It covers the principles of what constitutes frequent patterns, and details the methods for candidate generation and filtering. The goal is to demonstrate how log mining can lead to valuable insights and facilitate data-driven decisions.

1. Pattern Mining: Getting the
most out of your log data.
Krishna Sridhar
Staff Data Scientist, Dato Inc.
krishna_srd

2. • Background
- Machine Learning (ML) Research.
- Ph.D Numerical Optimization @Wisconsin
• Now
- Build ML tools for data-scientists & developers @Dato.
- Help deploy ML algorithms.
@krishna_srd, @DatoInc
About Me!

3. 45+$and$growing$fast!
About Us!

4. + =
Questions?
• (Now) I love questions. Feel free to interrupt for questions!
• (Later) Email me srikris@dato.com.
DAML Talks!

5. About you?

6. Creating a model pipeline
Ingest Transform Model Deploy
Unstructured Data
exploration
data
modeling
Data Science Workflow
Ingest Transform Model Deploy

7. Log Journey
Lots of data
Insights Profits

8. Log Mining: Pattern Mining

9. Logs are everywhere!

10. Machine Learning in Logs
Source: Mining Your Logs - Gaining Insight Through Visualization

11. Coffee shop
Coffee Shops Menu

12. Receipts
Coffee Shops Menu

13. Coffee Store Logs

14. Frequent Pattern Mining
What sets of items were bought together?

15. Real Applications

16. Real Applications

17. Real Applications

18. Log Mining: Rule Mining

19. Can we recommend items?
Rule Mining

20. Real Applications

21. Log Mining: Feature Extraction

22. Feature Extraction
0 1 0 0 0 0 1 1 0
1 1 0 0 1 0 0 0 0
0 0 1 1 1 0
Receipt Space Features in
Menu Space
ML

23. 3 Useful Data Mining Tasks
Rule MiningPattern Mining Feature Extraction

24. Demo

25. Pattern Mining: Explained

26. Formulating Pattern Mining
N distinct items → 2N itemsets

27. Formulating Pattern Mining
Find the top K most frequent sets of length at least L
that occur at least M times.

28. Formulating Pattern Mining
Find the top K most frequent sets of length at least L
that occur at least M times.
- max_patterns
- min_length
- min_support

29. Pattern Mining
N distinct items → 2N itemsets

30. Pattern Mining: Principles

31. Pattern Mining: Principles

32. Principle 1: What is frequent?
A pattern is frequent if it occurs at least M times.
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
Is the pattern {C, D} frequent?
M = 4
Patterns

33. Principle 1: What is frequent?
A pattern is frequent if it occurs at least M times.
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
{C, D} occurs 5 times
M = 4
Patterns

34. Principle 1: What is frequent?
A pattern is frequent if it occurs at least M times.
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
M = 4
Patterns
{C, D} occurs 5 times
Frequent!

35. Principle 1: What is frequent?
A pattern is frequent if it occurs at least M times.
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
Is the pattern {A, D} frequent?
M = 4
Patterns

36. Principle 1: What is frequent?
A pattern is frequent if it occurs at least M times.
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
M = 4
Patterns
{A, D} occurs 3 times.
Not frequent.

37. Principle 1: What is frequent?
A pattern is frequent if it occurs at least M times.
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
{C, D}: 5 is frequent
M = 4
{A, D}: 3 is not frequent
min_support

38. Principle 2: Apriori principle
A pattern is frequent only if a subset is frequent
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
{B, C, D} : 4 is frequent therefore
{C, D} : 5 is frequent
M = 4

39. Principle 2: Apriori principle
A pattern is frequent only if a subset is frequent
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
{B, C, D} : 4 is frequent therefore
{C, D} : 5 is frequent
M = 4
Why?
{C, D} must occur at least as
many times as {B, C, D}.

40. Principle 2: Apriori principle (Contrapositive)
If a pattern is not frequent then all supersets are not frequent
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
M = 4
{A} : 3 is not frequent therefore
{A, D} : 3 is not frequent

41. Principle 2: Apriori principle (Contrapositive)
If a pattern is not frequent then all supersets are not frequent
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
M = 4
{A} : 3 is not frequent therefore
{A, D} : 3 is not frequent
Why?
{A, D} cannot occur more times
than {A}.

42. Two Main Algorithms
• Candidate Generation
- Apriori
- Eclat
• Pattern Growth
- FP-Growth
- TopK FP-Growth

43. Candidate Generation

44. Lots of Generalizations
Source: http://www.philippe-fournier-viger.com/spmf/

45. Candidate Generation
Two phases
1. Candidate generation.
2. Candidate filtering.
Exploit Apriori Principle!

46. Candidate Generation
{AB} : ? {AC} : ? {AD} : ? {BC} : ? {BD} : ? {CD} : ?
{A} : ? {B} : ? {C} : ? {D} : ?
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}

47. Candidate Generation
{AB} : ? {AC} : ? {AD} : ? {BC} : ? {BD} : ? {CD} : ?
{A} : ? {B} : ? {C} : ? {D} : ?
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}

48. Candidate Generation
{AB} : ? {AC} : ? {AD} : ? {BC} : ? {BD} : ? {CD} : ?
{A} : 3 {B} : 4 {C} : 5 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}

49. Candidate Generation
{AB} : ? {AC} : ? {AD} : ? {BC} : ? {BD} : ? {CD} : ?
{A} : 3 {B} : 4 {C} : 5 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
Not frequent

50. Candidate Generation
{AB} : ? {AC} : ? {AD} : ? {BC} : ? {BD} : ? {CD} : ?
{A} : 3 {B} : 4 {C} : 5 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
No need to
explore!

51. Candidate Generation
{AB} : ? {AC} : ? {AD} : ? {BC} : ? {BD} : ? {CD} : ?
{A} : 3 {B} : 4 {C} : 5 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}

52. Candidate Generation
{AB} : ? {AC} : ? {AD} : ? {BC} : 4 {BD} : 4 {CD} : 5
{A} : 3 {B} : 4 {C} : 5 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}

53. Candidate Generation
{AB} : ? {AC} : ? {AD} : ? {BC} : 4 {BD} : 4 {CD} : 5
{A} : 3 {B} : 4 {C} : 5 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}

54. Two Main Algorithms
• Candidate Generation
- Apriori
- Eclat
• Pattern Growth
- FP-Growth
- TopK FP-Growth

55. Candidate Generation
Two phases
1. Candidate generation: Enumerate all subsets.
2. Candidate filtering: Eliminate infrequent subsets.
Exploit Apriori Principle!

56. Pattern Growth

57. Pattern Growth
Two phases
1. Candidate filtering
2. Conditional database constructions.
Avoid full scans over the data & large
candidate sets!

58. Pattern Growth - Depth First {B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
{AB} : 1 {AC} : 2 {AD} : 3 {BD} : 4 {CD} : 4
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
{ABC} : 0 {ABD} : 1 {ACD} : 2 {BCD} : 2
{BC} : 2

59. Pattern Growth - Preprocessing {B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
Preprocessing
First, count the number of times
each item (singleton) occurs.

60. Pattern Growth - Depth First {B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
{AB} : ? {AC} : ? {AD} : ? {BD} : ? {CD} : ?
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{BC} : ?

61. Pattern Growth - Depth First {B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
{AB} : ? {AC} : ? {AD} : ? {BD} : ? {CD} : ?
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{BC} : ?

62. Pattern Growth - Depth First {B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
{AB} : ? {AC} : ? {AD} : ? {BD} : ? {CD} : ?
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{BC} : ?

63. Pattern Growth - Depth First {B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
{AB} : ? {AC} : ? {AD} : ? {BD} : ? {CD} : ?
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{BC} : ?
No need to
explore!

64. Pattern Growth - Depth First {B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
{AB} : X {AC} : ? {AD} : ? {BD} : X {CD} : ?
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{BC} : X
Explore depth
first on {B}

65. Pattern Growth
{B} : 4
{ } : 6
Conditional Database Construction
DB{} DB{B}
{B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
{C, D}
{D}
{C, D}
{D}

66. Pattern Growth
{B} : 4
{ } : 6
Candidate Filtering
DB{B}
{C, D}
{D}
{C, D}
{D}
{D} : 4
{C} : 2
DB{}
{B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
DB{B}
Add {BD} as frequent

67. Pattern Growth - Depth First {C, D}
{D}
{C, D}
{D}
{AB} : X {AC} : ? {AD} : ? {BD} : 4 {CD} : ?
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{BC} : 2
Explore depth
first on {BD}

68. Pattern Growth - Depth First
{AB} : X {AC} : ? {AD} : ? {BD} : 4 {CD} : ?
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
{ABC} : ? {ABD} : X {ACD} : ? {BCD} : X
{BC} : 2

69. Compare & Constrast
• Candidate Generation
+ Better than brute force
+ Filters candidate sets
- Multiple passes over the data
• Pattern Growth
+ Fewer passes over the data
+ Space efficient.

70. Compare & Constrast
• Candidate Generation
+ Better than brute force
+ Filters candidate sets
- Multiple passes over the data
• Pattern Growth
+ Fewer passes over the data
+ Space efficient.
Better choice

71. Some cool ideas

72. FP-Tree Compression
Figures From Florian Verhein’s Slides on FP-Growth

73. FP-Growth Algorithm
Figures From Florian Verhein’s Slides on FP-Growth
Two phases
1. Candidate filtering.
2. Conditional database constructions.

74. TopK FP-Growth Algorithm
Similar to FP-Growth
1. Dynamically raise min_support.
2. Estimates of min_support greatly help.

75. Future Work

76. Distributed FP-Growth
Partition database on item-ids.
Database

77. Bags + Sequences
× 2
Itemset: {Item}
Bags: {Item: quantity}
Sequences : (item)

78. Demo: Model built, now what?

79. Summary
Log Data Mining
≠
Rocket Science
• FP-Growth for finding frequent patterns.
• Find rules from patterns to make predictions.
• Extract features for useful ML in pattern space.

80. SELECT questions FROM audience
WHERE difficulty == “Easy”
Thanks!