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# CS490D: Introduction to Data Mining Prof. Chris Clifton PowerPoint PPT Presentation

CS490D: Introduction to Data Mining Prof. Chris Clifton. April 19, 2004 More on Associations/Rules. Project: Association. Several people have suggested association rule mining Good idea Issues Most data is not “itemsets”, but scaled values Is apriori the right algorithm? Ideas

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

### Project: Association

• 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)

### Tertius

• 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

### Tertius: Use

• 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)

### JRIP/Ripper

• 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