Fast Algorithms for Mining Association Rules

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Fast Algorithms for Mining Association Rules

• Rakesh Agrawal and Ramakrishnan Srikant
• VLDB '94
• presented by
• kurt partridge
• cse 590db
• oct 4, 1999

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Mining Association Rules

• DB of "Basket Data"
• TID items
• 100 1 3 4
• 200 2 3 5
• 300 1 2 3 5
• 400 2 5

• association rules
• 1 gt 3
• 2,3 gt 5
• 2,5 gt 3

association rule metrics
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General Strategy

• Step I Find all itemsets with minimum support
  (minsup)
• Step II Generate rules from minsup'ed itemsets

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Step I Finding Minsup Itemsets

• Key fact Adding items to an itemset never
  increases its support
• General Strategy Proceed inductively on itemset
  size
• Apriori Algorithm
• 1. Base case Begin with all minsup itemsets of
  size 1 (L1)
• 2. Without peeking at the DB, generate candidate
  itemsets ofsize k (Ck) from Lk-1
• 3. Remove candidate itemsets that contain
  unsupported subsets
• 4. Further refine Ck using the database to
  produce Lk

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Algorithm to Guess Itemsets

• Naïve way
• Extend all itemsets with all possible items
• More sophisticated
• Join Lk-1 with itself, adding only a single,
  final item
• e.g. 1 2 3, 1 2 4, 1 3 4, 1 3 5, 2, 3,
  4 produces1 2 3 4 and 1 3 4 5
• Remove itemsets with an unsupported subset
• e.g. 1 3 4 5 has an unsupported subset 1 4
  5 if minsup 50
• Use the database to further refine Ck

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Example
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Part II Generating Rules

• Key fact
• Moving items from the antecedent to the
  consequent never changes support, and never
  increases confidence
• Algorithm
• For each itemset IS with minsup
• Find all minconf rules with a single consequent
  of the form (IS - L1 gt L1 )
• Guess candidate consequents Ck by appending items
  from IS - Lk-1 to Lk-1
• Verify confidence of each rule IS - Ck gt Ck
  using known itemset support values

repeat
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Other Details

• Itemset hash trees for subset testing
• Buffering
• Variations
• Fewer database passes, itemsets from multiple
  iterations
• AprioriTID -- exclude unnecessary database
  records
• AprioriHybrid -- use either Apriori or AprioriTID
  
• Future Work
• Multiple ISA Taxonomies
• constraints on rules (e.g. of items)

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Subsequent Papers

• Mining sequenced rules
• Finding "interesting" rules
• Efficiently handling long itemsets
• Integration with query optimizers
• Adjustments to handle dense/relational databases
• Apply constraints to further filter association
  rules

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Questions

• How are rules ranked? Do the minsup and minconf
  find interesting rules? Do they omit any
  interesting rules?
• What about maximum support?
• How well will this approach work for other
  problems (e.g. clustering, classification)?

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Apriori
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Apriori

• Join operation
• Subset filtering