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Data Mining Association Analysis: Basic Concepts and Algorithms

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1. Data Mining Association Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 6 (2) Introduction to Data Mining by Tan, Steinbach, Kumar. Rule Generation.

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Data Mining Association Analysis: Basic Concepts and Algorithms

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  1. © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 (2) Introduction to Data Mining by Tan, Steinbach, Kumar

  2. Rule Generation • Given a frequent itemset L, find all non-empty subsets f  L such that f  L – f satisfies the minimum confidence requirement • If {A,B,C,D} is a frequent itemset, candidate rules: ABC D, ABD C, ACD B, BCD A, A BCD, B ACD, C ABD, D ABCAB CD, AC  BD, AD  BC, BC AD, BD AC, CD AB, • If |L| = k, then there are 2k – 2 candidate association rules (ignoring L   and   L)

  3. Rule Generation • How to efficiently generate rules from frequent itemsets? • In general, confidence does not have an anti-monotone property c(ABC D) can be larger or smaller than c(AB D) • But confidence of rules generated from the same itemset has an anti-monotone property • e.g., L = {A,B,C,D}: c(ABC  D)  c(AB  CD)  c(A  BCD) • Confidence is anti-monotone w.r.t. number of items on the RHS of the rule

  4. Pruned Rules Rule Generation for Apriori Algorithm Lattice of rules Low Confidence Rule

  5. Rule Generation for Apriori Algorithm • Candidate rule is generated by merging two rules that share the same prefixin the rule consequent • join(CD=>AB,BD=>AC)would produce the candidaterule D => ABC • Prune rule D=>ABC if itssubset AD=>BC does not havehigh confidence

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