CS490D: Introduction to Data Mining Prof. Chris Clifton

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CS490D: Introduction to Data Mining Prof. Chris Clifton

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CS490D: Introduction to Data Mining Prof. Chris Clifton

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CS490D:Introduction to Data MiningProf. Chris Clifton

April 19, 2004

More on Associations/Rules

- Several people have suggested association rule mining
- Good idea

- Issues
- Most data is not “itemsets”, but scaled values
- Is apriori the right algorithm?

- Ideas
- Try Tertius
- Decision rules (need to define class)

- First-order logic learning
- Similar to ILP we have been discussing

- Provide structural schema definition
- Individual: What is an individual (for counting purposes)
- Structural: Define relationships between individuals
- Properties: Things that describe an individual

- http://www.cs.bris.ac.uk/Research/MachineLearning/Tertius/index.html

- Call: Specify number of literals, number of variables in rules
- Finds strongest k rules

- Predicate File
- Parent 2 person person cwa
- Daughter 2 person person cwa
- Male 1 person cwa

- Fact File
- Parent(Chris, Denise)
- Daughter(Denise, Chris)
- Female(Denise)

- Decision Rules
- Like association rules
- But need target class (right hand side)

- Idea:
- Grow rule
- Prune rule
- If good then keep
- Repeat

- Growth: Based on information gain
- Prune: p+N-n / P+N