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Classification based on Association Rules

Classification based on Association Rules. Introduction. Association rules were originally designed for finding multi-correlated items in transactions However, they can be easily adapted for classification.. How ?. Example.

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Classification based on Association Rules

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  1. Classification based on Association Rules

  2. Introduction • Association rules were originally designed for finding multi-correlated items in transactions • However, they can be easily adapted for classification.. • How ?

  3. Example Sepal Length (SL); Sepal Width (SW); Petal Length (PL); Petal Width (PW) Large = L; Medium = M; Small = S; Discretization of numeric attributes to create “Large”, “Medium”, “Small” Now apply Association rule mining to find patterns of the form: <features-sets> - Class Labels Rank rules first by confidence and then support

  4. Integration with Bayes Classifier • The frequent items generated for the frequent mining algorithm can be used as features and integrated into a Bayes classifier. • Suppose <f1,f2> is a frequent itemset in all transactions projected on class 1 (C1). • Eg. <f1,f2> appears in 20% of the transactions of C1 but only 5% of the transactions of C2. • Then <f1,f2> is a good candidate feature to try out in the Bayes classifier. • [This is part of the assignment]

  5. Integration with Bayesian Classifier • Suppose we have <SL=L,PW=M> as a frequent feature for Virginica. • Should we also have <SL=L> and <PW=M> as separate features ? • What are the pros and cons ?

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