Practice - Association Rule Mining (Apriori Algorithm: Support, Confidence, Lift)
Practice Questions
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Define Support in Association Rule Mining.
💡 Hint: Think about how often an itemset shows up in transactions.
What does Confidence measure in an association rule?
💡 Hint: Consider it as the reliability of the rule.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What is the purpose of Support in Association Rule Mining?
💡 Hint: Think about how frequently itemsets show up in your transactions.
True or False: A Lift value of less than 1 indicates a positive association between items.
💡 Hint: Recall what Lift tells us about the strength of association.
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Challenge Problems
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Consider a dataset containing transactions with items: [['milk', 'bread', 'butter'], ['milk', 'sugar'], ['bread', 'butter'], ['milk', 'bread', 'sugar', 'eggs']]. Apply the Apriori algorithm to find all frequent itemsets with a minimum support threshold of 0.5.
💡 Hint: Keep track of counts and ensure to apply the prune step effectively.
If you have a rule A⟹B with Support = 0.6, Confidence = 0.8, and Lift = 1.2, explain the implications of these values.
💡 Hint: Think in terms of how likely the association is compared to independent occurrence.
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