{ Mining, Sets, of, Patterns }
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1 { Mining, Sets, of, Patterns } A tutorial at ECMLPKDD2010 September 20, 2010, Barcelona, Spain by B. Bringmann, S. Nijssen, N. Tatti, J. Vreeken, A. Zimmermann 1
2 Overview Tutorial 00:00 00:45 Introduction Siegfried Nijssen Unsupervised, explorative pattern set mining Jilles Vreeken 01:30 Break 02:00 Supervised pattern set mining Björn Bringmann 02:45 End 2
3 Practical information Even though we did our best to achieve otherwise: WARNING This TUTORIAL is neither complete nor unbiased REFERENCES are not necessarily authoritative or complete More information (including references): 3
4 Part I Introduction 4
5 Overview part I Patterns Pattern sets Definitions Motivations Dimensions Algorithms 5
6 Overview part I Patterns Definitions Motivations Dimensions Algorithms 5
7 Overview part I Patterns Definitions Motivations Dimensions Algorithms 5
8 What is a pattern? Recurring structure Data Pattern 6
9 What is a pattern? 10,0 7,5 5,0 Data 2, ,5 5,0 7,5 10,0 Pattern y = x - 1 7
10 What is a pattern? 10,0 7,5 5,0 Data 2, ,5 5,0 7,5 10,0 Pattern y = x - 1 7
11 What is a pattern? In this tutorial we are looking for Recurring structures in enumerable, discrete domains Hence we do not consider a regression model to be a pattern... 8
12 Overview part I Patterns Definitions Motivations Dimensions Algorithms 9
13 Overview part I Patterns Definitions Motivations Dimensions Algorithms 9
14 What is a pattern? Example 1: Frequent Itemset in Market Basket Data 10
15 What is a pattern? Example 1: Frequent Itemset in Market Basket Data support( )=3 10
16 What is a pattern? Example 2: Co-cluster in Gene Expression Data Genes Conditions Lyssiotis et al. 11
17 What is a pattern? Example 3: Conjunctive Formula in UCI Data 4.9,3.1,1.5,0.1,Iris-setosa 5.0,3.2,1.2,0.2,Iris-setosa 5.5,3.5,1.3,0.2,Iris-setosa 4.9,3.1,1.5,0.1,Iris-setosa 4.4,3.0,1.3,0.2,Iris-setosa 5.1,3.4,1.5,0.2,Iris-setosa 5.0,3.5,1.3,0.3,Iris-setosa 4.5,2.3,1.3,0.3,Iris-setosa 4.4,3.2,1.3,0.2,Iris-setosa 5.0,3.5,1.6,0.6,Iris-setosa 5.1,3.8,1.9,0.4,Iris-setosa 4.8,3.0,1.4,0.3,Iris-setosa 5.1,3.8,1.6,0.2,Iris-setosa 4.6,3.2,1.4,0.2,Iris-setosa 5.3,3.7,1.5,0.2,Iris-setosa 5.0,3.3,1.4,0.2,Iris-setosa 7.0,3.2,4.7,1.4,Iris-versicolor 6.4,3.2,4.5,1.5,Iris-versicolor 6.9,3.1,4.9,1.5,Iris-versicolor 5.5,2.3,4.0,1.3,Iris-versicolor 6.5,2.8,4.6,1.5,Iris-versicolor 5.7,2.8,4.5,1.3,Iris-versicolor Petal length >= 2.0 and Petal width <=
18 What is a pattern? Example 4: Frequent Subgraph in Molecules 13
19 What is a pattern? Recurring structure in enumerable, discrete domain Enumerable, discrete domains: itemsets, graphs, sequences, trees,... Recurrence as determined by constraints: support constraint, size constraint, area constraint,... 14
20 The problem: too many patterns 15
21 Too many patterns... Solution 1: constraint-based mining Solution 2: pattern set mining 16
22 Overview part I Patterns Definitions Motivations Dimensions Algorithms 17
23 Overview part I Patterns Definitions Motivations Dimensions Algorithms 17
24 Solution 1: pattern constraints Constraint on each pattern individually based on background knowledge condensed representations class labels 18
25 Constraints: background knowledge Support constraints Syntactical constraints Statistical constraints difference with expectation taxonomies vs = diapers 19
26 Constraints: condensed representations If we pass a pattern through the data, we obtain another pattern derives 20
27 Constraints: condensed representations Closed patterns Pasquier et al. Free/generator patterns Pasquier et al. Maximal frequent patterns Bayardo Non-derivable patterns Calders et al. 21
28 Constraints: class labels vs 22
29 Constraints: class labels GIVEN database D, target c, threshold t, class of patterns FIND all patterns p with f(p,d,c)>t 23
30 Constraints: class labels Discriminative Pattern Subgroup Correlated Pattern Emerging Pattern 24
31 Constraints: class labels Many different names for this setting Novak, Webb and Lavrac Pattern name Emerging pattern Contrast set Correlated pattern Subgroup Discriminative pattern Class association rule Typical measure Growth rate Difference in rel support Chi2 Weighted relative accuracy Information gain Confidence Dong et al. Bay et al. Morishita et al. Kloesgen et al. Cheng et al. Liu et al. 25
32 Overview part I Patterns Definitions Motivations Dimensions Algorithms 26
33 Overview part I Patterns Definitions Motivations Dimensions Algorithms 26
34 How to find patterns? In principle two ways: Greedy / heuristic Fast Overlooks solutions Complete search Finds everything Slower 27
35 How to find patterns? Complete search under constraints often feasible GIVEN database D, constraint φ on D, class of patterns C FIND all patterns p in class C satisfying φ Key Observation: (Anti-)monotonicity 28
36 Lots of solutions... what s their problem? 29
37 Overview part I Patterns Motivations Definition Dimensions Algorithms 30
38 Overview part I Pattern sets Motivations Definition Dimensions Algorithms 30
39 The problem - complex pattern relationships Unsupervised descriptive task 31
40 The problem - complex pattern relationships Unsupervised descriptive task 31
41 The problem - complex pattern relationships Unsupervised descriptive task 31
42 The problem - complex pattern relationships Unsupervised descriptive task 31
43 The problem - complex pattern relationships Unsupervised descriptive task 31
44 The problem - complex pattern relationships Unsupervised descriptive task 31
45 The problem - complex pattern relationships Unsupervised descriptive task 31
46 The problem - complex pattern relationships Supervised predictive task 32
47 The problem - complex pattern relationships Supervised predictive task All patterns mined P1 P2 32
48 The problem - complex pattern relationships Supervised predictive task All patterns mined P1 P2 32
49 The problem - complex pattern relationships Supervised predictive task All patterns mined P1 P2 32
50 Overview part I Pattern sets Motivations Definitions Dimensions Algorithms 33
51 Overview part I Pattern sets Motivations Definitions Dimensions Algorithms 33
52 Pattern set mining GIVEN a data mining task FIND an interrelated set of patterns useful for this task 34
53 Overview part I Pattern sets Motivations Definition Dimensions Algorithms 35
54 Overview part I Pattern sets Motivations Definition Dimensions Algorithms 35
55 Patterns vs Pattern sets unsupervised supervised pattern mining no target no relationships relevant to target no relationships pattern set mining no target relationships part II relevant to target relationships part III 36
56 Task dimensions unsupervised supervised descriptive Association Analysis Tiling (Co-)Clustering Probabilistic models Subgroup discovery Exceptional model mining predictive Predictive clustering part II Classification Regression part III 37
57 Task dimensions Supervised vs unsupervised Predictive vs descriptive 38
58 Task dimensions Supervised vs unsupervised Predictive vs descriptive (Semi-)Structured data vs Binary data 38
59 Task dimensions Supervised vs unsupervised Predictive vs descriptive (Semi-)Structured data vs Binary data vs 38
60 Task dimensions Supervised vs unsupervised Predictive vs descriptive (Semi-)Structured data vs Binary data 38
61 Task dimensions Supervised vs unsupervised Predictive vs descriptive (Semi-)Structured data vs Binary data Constrained vs Unconstrained 38
62 Task dimensions Supervised vs unsupervised Predictive vs descriptive (Semi-)Structured data vs Binary data Constrained vs Unconstrained vs 38
63 Task dimensions Supervised vs unsupervised Predictive vs descriptive (Semi-)Structured data vs Binary data Constrained vs Unconstrained 38
64 Task dimensions Supervised vs unsupervised Predictive vs descriptive (Semi-)Structured data vs Binary data Constrained vs Unconstrained Interpretable model vs Black box 38
65 Overview part I Pattern sets Motivations Definitions Dimensions Algorithms 39
66 Overview part I Pattern sets Motivations Definitions Dimensions Algorithms 39
67 How to find pattern sets? Mining Constraint DB Pattern Mining PS Pattern Selection PS Model Induction M Optimisation Criteria 40
68 How to find pattern sets? Pattern set constraint Mining Constraint DB Pattern Mining PS Pattern Selection PS Model Induction M Optimisation Criteria 40
69 How to find pattern sets? Pattern set constraint Mining Constraint DB Pattern Mining PS Pattern Selection PS Model Induction M Optimisation Criteria Model constraint 40
70 How to find pattern sets? Pattern set constraint Mining Constraint DB Pattern Mining PS Pattern Selection PS Model Induction M Optimisation Criteria Model constraint 40
71 How to find pattern sets? Pattern set constraint Mining Constraint DB Pattern Mining PS Pattern Selection PS Model Induction M Optimisation Criteria Model constraint 40
72 How to find pattern sets? Pattern set constraint Pattern set constraint Mining Constraint DB Pattern Mining PS Pattern Selection PS Model Induction M Optimisation Criteria Model constraint Model constraint 41
73 How to find pattern sets? 1 Model Independent Iterative Mining Pattern set constraint Mining Constraint DB Pattern Mining PS Pattern Selection PS Model Induction M Optimisation Criteria Model constraint Model constraint 41
74 How to find pattern sets? 1 Model Independent Iterative Mining Pattern set constraint 2 Model Independent Post Processing Mining Constraint DB Pattern Mining PS Pattern Selection PS Model Induction M Optimisation Criteria Model constraint Model constraint 41
75 How to find pattern sets? 1 Model Independent Iterative Mining Pattern set constraint 2 Model Independent Post Processing Mining Constraint DB Pattern Mining PS Pattern Selection PS Model Induction M 3 Model Dependent Iterative Mining Optimisation Criteria Model constraint 41
76 How to find pattern sets? 1 Model Independent Iterative Mining Pattern set constraint 2 Model Independent Post Processing Mining Constraint DB Pattern Mining PS Pattern Selection PS Model Induction M 3 Model Dependent Iterative Mining 4 Model Dependent Post Processing Optimisation Criteria Model constraint 41
77 Pattern set = Feature set 42
78 Pattern set = Feature set 42
79 Feature vs pattern selection Feature Selection Binary Feature Selection Pattern Selection We know more about patterns constraints used generality relationships 43
80 Overview Unsupervised Pattern set mining Part II Supervised Pattern set mining Part III How to score pattern sets How to find pattern sets 44
81 End of Part I 45
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