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CS490D: Introduction to Data Mining Prof. Chris Clifton

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

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CS490D: Introduction to Data Mining Prof. Chris Clifton

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  1. CS490D:Introduction to Data MiningProf. Chris Clifton April 19, 2004 More on Associations/Rules

  2. 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)

  3. 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

  4. 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)

  5. 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

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