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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

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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