数据挖掘-Chapter5-购物篮分析与关联规则要点.ppt

* Chapter 5 Market Basket Analysis and Association Rules Market Basket Analysis What can be inferred? I purchase diapers I purchase a new car I purchase OTC cough medicine I purchase a prescription medication I don’t show up for class Market Basket Analysis Retail – each customer purchases different set of products, different quantities, different times MBA uses this information to: Identify who customers are (not by name) Understand why they make certain purchases Gain insight about its merchandise (products): Fast and slow movers Products which are purchased together Products which might benefit from promotion Take action: Store layouts Which products to put on specials, promote, coupons… Combining all of this with a customer loyalty card it becomes even more valuable Association Rules Data Mining technique most closely allied with Market Basket Analysis Association Rules can be automatically generated AR represent patterns in the data without a specified target variable Good example of undirected data mining Whether patterns make sense is up to humanoids (us!) Association Rules Apply Elsewhere Besides retail – supermarkets, etc… Purchases made using credit/debit cards Optional Telco Service purchases Banking services Unusual combinations of insurance claims can be a warning of fraud Medical patient histories Market Basket Analysis Drill-Down MBA is a set of techniques, Association Rules being most common, that focus on point-of-sale (p-o-s) transaction data 3 types of market basket data (p-o-s data) Customers Orders (basic purchase data) Items (merchandise/services purchased) Typical Data Structure (Relational Database) Lots of questions can be answered Avg # of orders/customer Avg # unique items/order Avg # of items/order For a product What % of customers have purchased Avg # orders/customer include it Avg quantity of it purchased/order Etc… Visualization is extremely helpful…next slide Transaction Data Sales Order Characteristics 订单数常常是划分客户的有用方式 好的客户明显比不好的客户