Goal and key features a Goal: Find all rules that satisfy the user specified minimum support(minsup) and minimum confidence(minconf) a Key Features o Completeness: find all rules No target item(s)on the right-hand-side 7
11 Goal and key features ◼ Goal: Find all rules that satisfy the userspecified minimum support (minsup) and minimum confidence (minconf). ◼ Key Features ◆ Completeness: find all rules. ◆ No target item(s) on the right-hand-side
An example t1: Beef. chicken Milk t2: Beef. cheese t 3: Cheese, Boots Transaction data t4: Beef chicken, Cheese Assume t5: Beef chicken clothes Cheese milk minsup= 30% t6: Chicken clothes milk minconf= 80% t7: Chicken. Milk. Clothes a An example frequent itemset [Chicken, Clothes, Milk] [sup= 3/7 Association rules from the itemset Clothes->Milk, Chicken [sup= 3/7, conf = 3 /3] Clothes, Chicken->Milk, [sup= 3/7, conf =3/3
12 An example ◼ Transaction data ◼ Assume: minsup = 30% minconf = 80% ◼ An example frequent itemset: {Chicken, Clothes, Milk} [sup = 3/7] ◼ Association rules from the itemset: Clothes → Milk, Chicken [sup = 3/7, conf = 3/3] … … Clothes, Chicken→ Milk, [sup = 3/7, conf = 3/3] t1: Beef, Chicken, Milk t2: Beef, Cheese t3: Cheese, Boots t4: Beef, Chicken, Cheese t5: Beef, Chicken, Clothes, Cheese, Milk t6: Chicken, Clothes, Milk t7: Chicken, Milk, Clothes
Transaction data representation aA simplistic view of shopping baskets, a Some important information not considered the quantity of each item purchased and ◆ the price paid
13 Transaction data representation ◼ A simplistic view of shopping baskets, ◼ Some important information not considered. E.g, ◆ the quantity of each item purchased and ◆ the price paid
Many mining algorithms There are a large number of them They use different strategies and data structures Their resulting sets of rules are all the same Given a transaction data set T, and a minimum support and a minimum confident the set of association rules existing in T is uniquely determined a Any algorithm should find the same set of rules although their computational efficiencies and memory requirements may be different a We study only one: the Apriori algorithm
14 Many mining algorithms ◼ There are a large number of them!! ◼ They use different strategies and data structures. ◼ Their resulting sets of rules are all the same. ◆ Given a transaction data set T, and a minimum support and a minimum confident, the set of association rules existing in T is uniquely determined. ◼ Any algorithm should find the same set of rules although their computational efficiencies and memory requirements may be different. ◼ We study only one: the Apriori Algorithm
Road map ■ Basic concepts ■ Apriori algorithm Different data formats for mining a Mining with multiple minimum supports a Mining class association rules Summary
15 Road map ◼ Basic concepts ◼ Apriori algorithm ◼ Different data formats for mining ◼ Mining with multiple minimum supports ◼ Mining class association rules ◼ Summary