1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Association Rule Mining II Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential.

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1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Association Rule Mining II Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential Chair of Business

2 Limitation of Confidence and Support TID Items 1Game, VCR 2 Game, VCR 3 Game, VCR 4 Game, VCR 5 Game 6 VCR 7 VCR 8 VCR 9 VCR 10 PC Minimum support = 20% Minimum confidence = 50% Support (Game  VCR) = 4/10=40% Confidence (Game  VCR) = 4/5=80% Is the rule interesting??? Support({VCR})=8/10=80% Confidence ((NOT Game)  VCR) = 4/5=80% Game and VCR are independent!! Rule Game  VCR is misleading!!!

3 Negative Correlation TID Items 1Game, VCR 2 Game, VCR 3 Game, VCR 4 VCR 5 Game 6 VCR 7 VCR 8 VCR 9 VCR 10Game Support (Game  VCR) = 3/10=30% Confidence (Game  VCR) = 3/5=60% Is the rule interesting??? Support({VCR})=8/10=80% Confidence ((NOT Game)  VCR) = 5/5=100% Game and VCR are negatively correlated!! Rule Game  VCR is misleading!!! Minimum support = 20% Minimum confidence = 50%

4 Positive Correlation TID Items 1Game, VCR 2 Game, VCR 3 Game, VCR 4 Game, VCR 5 PC 6 PC 7 VCR 8 VCR 9 VCR 10 Game, VCR Support (Game  VCR) = 5/10=50% Confidence (Game  VCR) = 5/5=100% Is the rule interesting??? Support({VCR})=8/10=80% Confidence ((NOT Game)  VCR) = 3/5=60% Game and VCR are POSITIVELY correlated!! Rule Game  VCR is interesting!!! Minimum support = 20% Minimum confidence = 50%

5 Another Measurement: LIFT Lift of an association rule X  Y is defined as Lift (X  Y)=conf(X  Y )/supp(Y) If Lift (X  Y)=1 then X and Y are independent If Lift (X  Y)< 1, then X and Y are negative correlated If Lift (X  Y)>1, then X and Y are positive correlated Interesting association rules have lift larger than 1.

6 Types of Association Rules Single-dimensional association rule: associate one type of item e.g., presence, absence or buy association rules in market basket analysis Multi-dimensional association rule: associate multiple types of items e.g., age, income, buy, etc. Single-level association rule: for items at the same level of abstraction e.g., Shamrock milk  Iron Kids bread or milk  bread Multiple-level association rule: for items at multiple levels of abstraction e.g., both Shamrock milk  Iron Kids bread and milk  bread