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Association Rule

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Submitted By laurar
Words 429
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tomatoes -> lettuce
[Coverage=0.263 (263); Support=0.111 (111); Strength=0.422; Lift=1.94; Leverage=0.0539 (53.9); p=2.35E-019]

lettuce -> tomatoes
[Coverage=0.217 (217); Support=0.111 (111); Strength=0.512; Lift=1.94; Leverage=0.0539 (53.9); p=2.35E-019]

tomatoes -> carrots
[Coverage=0.263 (263); Support=0.085 (85); Strength=0.323; Lift=1.85; Leverage=0.0390 (39.0); p=1.83E-012]

carrots -> tomatoes
[Coverage=0.175 (175); Support=0.085 (85); Strength=0.486; Lift=1.85; Leverage=0.0390 (39.0); p=1.83E-012]

onions -> potatoes
[Coverage=0.189 (189); Support=0.082 (82); Strength=0.434; Lift=1.53; Leverage=0.0285 (28.5); p=5.30E-007]

Assignment 1: Association Rules
Association rules represent a learning method to discover relations and associations between groups of data. The purpose of the association rules is to find certain patterns in the items in a large database. This will enable us to discover the probability that one would buy a product, given the purchase of another product. There is a certain terminology and notations for the association theory. The support of a set of items represents the number of transactions in which a certain set of items occurs in the transaction file. The confidence of a rule will show how representative or how significant a certain rule is. This is an absolute measure. The lift is a relative measure which will enable us to interpret the importance of a rule. It compares the degree of dependence in a rule versus independence between the consequent items and the antecedent items. If the lift is close to 1, this will mean that there is no association between two items or sets. If the lift is greater than 1, there will be a positive association between two items or sets. And finally if the lift is less than 1, there will be a negative association between two items or sets. Discovering meaningful rules from a large set of data is an impossible task. This is why we use algorithms to search these rules. To find good association rules, the search method called “A priori Algorithm” is used. We should try to find several minimal support and confidence requirements if we are building a meaningful set of association rules. If the A priori Algorithm produces an association rule that is not interpretable or intuitive in the face of domain-specific knowledge, it need not be adopted and used for decision-making purposes.

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