Cs490d introduction to data mining prof chris clifton
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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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Cs490d introduction to data mining prof chris clifton

CS490D:Introduction to Data MiningProf. Chris Clifton

April 19, 2004

More on Associations/Rules


Project association

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

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

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

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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