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Advanced Techniques in Association Rule Mining and Decision Rule Learning

This lecture focuses on advanced concepts in association rule mining, exploring the challenges faced when handling non-itemset data and discussing the appropriateness of using the Apriori algorithm. It introduces Tertius, a system for learning decision rules based on first-order logic, emphasizing the importance of defining structural relationships and individual properties. The session covers the generation and pruning of decision rules, demonstrating how these can be effectively implemented using information gain to enhance data mining results.

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Advanced Techniques in Association Rule Mining and Decision Rule Learning

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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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