DWDM \ Association Mining RulesAssociation Mining Rules in data mining are used to find relationships among data items in sets.Example: Market Based Analysis(It allows retailers to identify relationships between the items that people buy together frequently). Association Rule It is used to predict the occurrence of an item based on the occurrences of other items in the set of transactions. i.e X → Y [Form of Implication expression], where X and Y are item sets. Example: {Milk, Diaper} → {Beer} I.e. persons who buy milk and diaper will automatically buy beer. Rule Evaluation Metrics An implication expression of the form X → Y, where X and Y are itemsets. Example: {Milk, Diaper} → {Beer}. Rule Evaluation Metrics
Example
Example: {milk, Diaper} »Beer for suport and confidence calculation. Support s = σ (milk, Diaper, Beer) / Total Number of Transactions (T) = 2 / 5= 0.4 Confidence = σ (milk, Diaper, Beer) / σ (milk, Diaper) = 2 / 3 = 0.67 Mining Association Rules Two-step approach 1. Frequent Itemset Generation : i.e itemsets whose support ≥ minsup. 2. Rule Generation :Generate confidence(Rule is a binary partitioning of a frequent itemset.) rules from each frequent itemset. |
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