重庆大学：《数据仓库与数据挖掘 Data Warehouse and Data mining》课程PPT教学课件（英文版）Chapter 5 Mining Frequent Patterns, Association and Correlations：Basic Concepts and Methods

Chapter 5: Mining Frequent Patterns, Association and Correlations: Basic Concepts and Methods ■ Basic concepts Frequent itemset Mining methods Which Patterns Are Interesting?Pattern Evaluation methods Summary

1 Chapter 5: Mining Frequent Patterns, Association and Correlations: Basic Concepts and Methods ◼ Basic Concepts ◼ Frequent Itemset Mining Methods ◼ Which Patterns Are Interesting?—Pattern Evaluation Methods ◼ Summary

What Is Frequent Pattern Analysis? Frequent pattern a pattern(a set of items subsequences substructures etc. that occurs frequently in a data set First proposed by agrawal, Imielinski, and Swami [ais93] in the context of frequent itemsets and association rule mining Motivation Finding inherent regularities in data What products were often purchased together? Beer and diapers? What are the subsequent purchases after buying a pc? What kinds of dna are sensitive to this new drug? Can we automatically classify web documents? Applications Basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log ( click stream) analysis and dna sequence analysis

2 What Is Frequent Pattern Analysis? ◼ Frequent pattern: a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set ◼ First proposed by Agrawal, Imielinski, and Swami [AIS93] in the context of frequent itemsets and association rule mining ◼ Motivation: Finding inherent regularities in data ◼ What products were often purchased together?— Beer and diapers?! ◼ What are the subsequent purchases after buying a PC? ◼ What kinds of DNA are sensitive to this new drug? ◼ Can we automatically classify web documents? ◼ Applications ◼ Basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis

Association Rule Mining Given a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction Market-Basket transactions Exam ple of Association Rules TD ltems Bread. milk [Diaper> Beer), MIlk, Bread]>[Eggs, Coke), Bread, Diaper, Beer, Eggs Beer, Bread>(Milk 345 Milk, Diaper, Beer, Coke Bread, Milk, Diaper, beer Implication means co-occurrence Bread, Milk, Diaper, Coke not causality! 3

3 Association Rule Mining ◼ Given a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction Market-Basket transactions TID Items 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke Example of Association Rules {Diaper} → {Beer}, {Milk, Bread} → {Eggs,Coke}, {Beer, Bread} → {Milk}, Implication means co-occurrence, not causality!

Why Is Freq Pattern Mining Important? Freq pattern An intrinsic and important property of datasets Foundation for many essential data mining tasks Association, correlation, and causality analysis Sequential, structural (e. g, sub-graph) patterns Pattern analysis in spatiotemporal, multimedia, time series, and stream data Classification: discriminative, frequent pattern analysis Cluster analysis: frequent pattern-based clustering Data warehousing iceberg cube and cube-gradient Semantic data compression fascicles Broad applications

4 Why Is Freq. Pattern Mining Important? ◼ Freq. pattern: An intrinsic and important property of datasets ◼ Foundation for many essential data mining tasks ◼ Association, correlation, and causality analysis ◼ Sequential, structural (e.g., sub-graph) patterns ◼ Pattern analysis in spatiotemporal, multimedia, time￾series, and stream data ◼ Classification: discriminative, frequent pattern analysis ◼ Cluster analysis: frequent pattern-based clustering ◼ Data warehousing: iceberg cube and cube-gradient ◼ Semantic data compression: fascicles ◼ Broad applications

Basic Concepts: Frequent Patterns id Items bought a itemset: a set of one or more Beer, Nuts, Diaper items 20 Beer, Coffee, Diaper k- itemset x={x1…,X} 30 Beer, Diaper, Eggs absolute) support, or, support 40 Nuts, Eggs, Milk count of X: Frequency or 50Nuts, Coffee, Diaper, Eggs, Milk occurrence of an itemset x Customer Customer (relative)support, s, is the buys both buys diaper fraction of transactions that contains X(i.e. the probability that a transaction contains X) An itemset X is frequent if Xs support is no less than a minsup Customer threshold buys beer 5

5 Basic Concepts: Frequent Patterns ◼ itemset: A set of one or more items ◼ k-itemset X = {x1 , …, xk} ◼ (absolute) support, or, support count of X: Frequency or occurrence of an itemset X ◼ (relative) support, s, is the fraction of transactions that contains X (i.e., the probability that a transaction contains X) ◼ An itemset X is frequent if X’s support is no less than a minsup threshold Customer buys diaper Customer buys both Customer buys beer Tid Items bought 10 Beer, Nuts, Diaper 20 Beer, Coffee, Diaper 30 Beer, Diaper, Eggs 40 Nuts, Eggs, Milk 50 Nuts, Coffee, Diaper, Eggs, Milk

Basic Concepts: Association Rules Tid Items bought Find all the rulesⅩ→ywit Beer, Nuts diaper 0000 minimum support and confidence Beer, Coffee, diaper Beer, Diaper, eggs support s, probability that a Nuts, Eggs, Milk transaction contains xu y 50Nuts, Coffee, Diaper, Eggs,Milk confidence, c conditional Customer Customer probability that a transaction lyS diaper having x also contains r Let minsup= 50%, minconf=50% Freg. Pat: Beer: 3, Nuts: 3, Diaper: 4, Eggs: 3, Customer Beer, Diaper]: 3 buys beer Association rules: (many more!) Beer> Diaper(60%, 100%) Diaper> Beer(60%,75%) 6

6 Basic Concepts: Association Rules ◼ Find all the rules X → Y with minimum support and confidence ◼ support, s, probability that a transaction contains X  Y ◼ confidence, c, conditional probability that a transaction having X also contains Y Let minsup = 50%, minconf = 50% Freq. Pat.: Beer:3, Nuts:3, Diaper:4, Eggs:3, {Beer, Diaper}:3 Customer buys diaper Customer buys both Customer buys beer 40 Nuts, Eggs, Milk 50 Nuts, Coffee, Diaper, Eggs, Milk 30 Beer, Diaper, Eggs 20 Beer, Coffee, Diaper 10 Beer, Nuts, Diaper Tid Items bought ◼ Association rules: (many more!) ◼ Beer → Diaper (60%, 100%) ◼ Diaper → Beer (60%, 75%)

Basic Concepts: Frequent Patterns and Association rules Itemset X={X1,…× Find all the rules x with minimum support and confidence support,, s,probability sup port(X→Y)=P(r∪F) that a transaction contains x∪Y confidence, c conditional probability that a confidence(→=P(F|X) transaction having x also contains y

7 Basic Concepts: Frequent Patterns and Association Rules ◼ Itemset X = {x1 , …, xk} ◼ Find all the rules X → Y with minimum support and confidence ◼ support, s, probability that a transaction contains X  Y ◼ confidence, c, conditional probability that a transaction having X also contains Y sup port(X  Y) = P(X  Y ) confidence(X  Y ) = P(Y | X )

Basic Concepts: Frequent Patterns and Association Rules Transaction-id Items bought Let Supmin =50%, confmin =50% 10 A,B D 20 A,C,D Minimum support number is 3 30 A, D,E B, EF Freg. Pat: A: 3, B: 3, D: 4 E 3, AD: 3 50 B, C.DEF Association rules Customer Customer buys both A→D(60%,100%)3/5,1) buys diaper D→A(60%75%)3/5,3/4) Strong association rule Customer buys beer 8

8 Basic Concepts: Frequent Patterns and Association Rules Let supmin = 50%, confmin = 50% Minimum support number is 3 Freq. Pat.: {A:3, B:3, D:4, E:3, AD:3} Association rules: A → D (60%, 100%)(3/5,1) D → A (60%, 75%)(3/5,3/4) Strong association rule Customer buys diaper Customer buys both Customer buys beer Transaction-id Items bought 10 A, B, D 20 A, C, D 30 A, D, E 40 B, E, F 50 B, C, D, E, F

Association Rule Mining Task Given a set of transactions T, the goal of association rule mining is to find all rules having support> minsup threshold confidence minconf threshold Brute-force approach a List all possible association rules Compute the support and confidence for each rule Prune rules that fail the minsup and minconf thresholds Computationally prohibitive

9 Association Rule Mining Task ◼ Given a set of transactions T, the goal of association rule mining is to find all rules having ◼ support ≥ minsup threshold ◼ confidence ≥ minconf threshold ◼ Brute-force approach: ◼ List all possible association rules ◼ Compute the support and confidence for each rule ◼ Prune rules that fail the minsup and minconf thresholds  Computationally prohibitive!

Mining Association Rules Example of Rules TD ltems Bread. milk MIlk,Diaper]> Beer)(s=0. 4, C=0.67) Bread, Diaper, Beer, eggs MIlk, Beer]->Diaper](s=0.4, C=1.0) 2345 Milk, Diaper, Beer, Coke [Diaper, Beer]->Milk](s=0.4, C=0.67) Bread, Milk, Diaper, beer Beer]>Milk, Diaper](s=0.4,C=0.67) DIaper)>Milk, Beer)(s=0. 4, C=0.5) Bread, Milk, Diaper, Coke (Milk)>Diaper, Beer (s=0.4, c=0.5) Observations All the above rules are binary partitions of the same itemset Milk, Diaper, Beery Rules originating from the same itemset have identical support but can have different confidence Thus, we may decouple the support and confidence requirements

10 Mining Association Rules Example of Rules: {Milk,Diaper} → {Beer} (s=0.4, c=0.67) {Milk,Beer} → {Diaper} (s=0.4, c=1.0) {Diaper,Beer} → {Milk} (s=0.4, c=0.67) {Beer} → {Milk,Diaper} (s=0.4, c=0.67) {Diaper} → {Milk,Beer} (s=0.4, c=0.5) {Milk} → {Diaper,Beer} (s=0.4, c=0.5) TID Items 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke Observations: • All the above rules are binary partitions of the same itemset: {Milk, Diaper, Beer} • Rules originating from the same itemset have identical support but can have different confidence • Thus, we may decouple the support and confidence requirements