Association Rule Mining(ARM) notes for class
- 2. Association Rule Mining (ARM) • The classical problem of association pattern mining is defined in the context of supermarket data containing sets of items bought by customers, which are referred to as transactions. • The goal is to determine associations between groups of items bought by customers. • The most popular model for association pattern mining uses the frequencies of sets. • The discovered sets of items are referred to as large itemsets, frequent itemsets, or frequent patterns. • Application of ARM - Supermarket data, Text mining, Generalization to dependency-oriented data types, Market Basket Analysis.
- 3. Association Rule Mining (ARM) • Frequent itemsets can be used to generate association rules of the form X ⇒ Y , where X and Y are sets of items. • This rule suggests that buying of X makes it more likely that Y will also be bought. • Given a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction. • Example of Association Rules {Diaper} → {Beer}, {Milk, Bread} → {Eggs, Coke}, {Beer, Bread} → {Milk},
- 4. Association Rule Mining (ARM) • Itemset • A collection of one or more items, eg; {Milk, Bread, Diaper} • k-itemset - An itemset that contains k items. • Support count (σ) • Frequency of occurrence of an itemset • E.g. σ({Milk, Bread,Diaper}) = 2 • Support • Fraction of transactions that contain an itemset • E.g. s({Milk, Bread, Diaper}) = 2/5 • Frequent Itemset • An itemset whose support is greater than or equal to a minsup threshold
- 5. Association Rule Mining (ARM) • Rule Evaluation Metrics • Support (s) • Fraction of transactions that contain both X and Y • The support of an itemset I is defined as the fraction of the transactions in the database T = {T1 . . . Tn} that contain I as a subset. • Confidence (c) • Measures how often items in Y appear in transactions that contain X. • The confidence conf(X ∪ Y ) of the rule X ∪ Y is the conditional probability of X ∪ Y occurring in a transaction, given that the transaction contains X. Therefore, the confidence conf(X ⇒ Y ) is defined as follows – • Association Rule Mining Task • Given a set of transactions T, the goal of association rule mining is to find all rules having • support ≥ minsup threshold • confidence ≥ minconf threshold
- 6. Association Rule Mining Task • Brute-force approach: • List all possible association rules • Compute the support and confidence for each rule • Prune rules that fail the minsup and minconf thresholds. • Computationally Expensive • Strategies to reduce Computational Complexity: • Reduce the number of candidates (M) • Reduce the number of transactions (N) • Reduce the number of comparisons (NM)
- 7. Association Rule Mining Task • Apriori Principle: If an itemset is frequent, then all of its subsets must also be frequent. • Apriori principle holds due to the following property of the support measure: • Support of an itemset never exceeds the support of its subsets. • This is known as the anti-monotone property of support. • Apriori also uses the downward closure property. • Apriori algorithm generates candidates with smaller length k first and counts their supports before generating candidates of length (k+1). • The resulting frequent k-itemsets are used to restrict the number of (k + 1)- candidates with the downward closure property.
- 8. Association Rule Mining Task