Analysis of Example 2 Given: a database of customer transactions(e.g.,shopping baskets),where each transaction is a set of items (e.g.,products purchased during a visit). Find: groups of items which are frequently purchased together(customers purchasing behaviour). 米 for example,"IF buys beer and sausage,THEN also buys mustard with high probability." ● Information that can suggest... new store layouts and product assortments,or which products to put on promotion. 14 DATA Copyright 2019 by Xiaoyu Li
14 Copyright © 2019 by Xiaoyu Li. Analysis of Example 2
Analysis of Example 3 The found information on customers purchasing behaviour can be useful: -"On Thursdays,grocery store consumers often purchase diapers and beer together." trivial: -"Customers who purchase maintenance agreements are very likely to purchase large appliances." unexplicable/unexpected: "When a new hardware store opens,one of the most sold items is toilet rings." 15 DATA Copyright 2019 by Xiaoyu Li
15 Copyright © 2019 by Xiaoyu Li. Analysis of Example 3
Types of Association Rules Different types of association rules based on Types of values handled -Boolean association rules Mainly these! Quantitative association rules Levels of abstraction involved -Single-level association rules Multilevel association rules Dimensions of data involved -Single-dimensional association rules Multidimensional association rules 16 DATA Copyright 2019 by Xiaoyu Li
16 Copyright © 2019 by Xiaoyu Li. Types of Association Rules
Basic Concepts of Association Rules Data considered is transactional or relational. Each transaction or row consists of an identifier and a set of items. For example Id Itemsets 1 Item1,Item2 2 Item3 3 Item2,Item5 Boolean association rules: 米 Each item can be seen as a Boolean variable presenting the presence or absence of that item in the transaction/row. DATA 17 Copyright 2019 by Xiaoyu Li
17 Copyright © 2019 by Xiaoyu Li. Basic Concepts of Association Rules