电子科技大学：《数据分析与数据挖掘 Data Analysis and Data Mining》课程教学资源（课件讲稿）Lecture 04 Association Rules of Data Reasoning

Lecture 4 Association Rules of Data Reasoning Dr.李晓瑜Xiaoyu Li Email:xiaoyuuestc@uestc.edu.cn http://blog.sciencenet.cn/u/uestc2014xiaoyu 2019-Spring SunData Group http://www.sundatagroup.org School of Information and Software Engineering,UESTC 1966 Copyright2019 by Xiaoyu Li

Dr.李晓瑜 Xiaoyu Li Email：xiaoyuuestc@uestc.edu.cn http://blog.sciencenet.cn/u/uestc2014xiaoyu 2019-Spring Lecture 4 Association Rules of Data Reasoning SunData Group http://www.sundatagroup.org/ School of Information and Software Engineering, UESTC Copyright © 2019 by Xiaoyu Li. 1

S1at3Go是10 Content (5H) 4.1 The basic concept of association rules 4.2 Low-dimensional binary association rules .4.3 Multi-level association rules 4.4 Multidimensional association rules 4.5 The Affinity analysis based on the association mining 5 Copyright 2019 by Xiaoyu Li

Content（5H）  4.1 The basic concept of association rules  4.2 Low-dimensional binary association rules  4.3 Multi-level association rules  4.4 Multidimensional association rules  4.5 The Affinity analysis based on the association mining Copyright © 2019 by Xiaoyu Li. 5

Group Target DATA Based on Apriori algorithm of low dimensional binary association rule learning. Multidimensional association rules discovery and learning. 6 Copyright 2019 by Xiaoyu Li

Target  Based on Apriori algorithm of low dimensional binary association rule learning.  Multidimensional association rules discovery and learning. Copyright © 2019 by Xiaoyu Li. 6

Association rules Part IⅡ Introduction Non-Structured 。Raw data ·Regression 。 Key problem ·Pre-processing ●Association ·Conclusion ·Clustering ·Classification Part I Part Fig.1.Review the structure of course 7 DATA Copyright 2019 by Xiaoyu Li

7 • Introduction • Raw data • Pre-processing Part I • Regression •Association • Clustering • Classification Part II • Non-Structured • Key problem • Conclusion Part III Fig.1. Review the structure of course Copyright © 2019 by Xiaoyu Li. Association Rules

Example Which items are frequently purchased together by customers? Shopping Baskets bread milk bread milk bread milk cereal sugar eggs butter Customer 1 Customer 2 Customer 3 sugar eggs Market Analyst Customer n DATA 8 Copyright 2019 by Xiaoyu Li

Copyright © 2019 by Xiaoyu Li. 8 Example

Analysis of shopping basket Market-Basket transactions Example of Association Rules TID Items Bread,Milk Diaper})→{Beer, (Milk,Bread)[Eggs,Coke}, 2 Bread,Diaper,Beer,Eggs {Beer,Bread)→{Milk, Milk,Diaper,Beer,Coke Bread,Milk,Diaper,Beer Implication means co-occurrence, Bread,Milk,Diaper,Coke not causality! 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 ATA 9 Copyright 2019 by Xiaoyu Li

9 Copyright © 2019 by Xiaoyu Li. Analysis of shopping basket

Association Rules What are association rules? Boolean association rules Overview Multilevel association rules Quantitative association rules Multidimensional association rules Constraint-based association mining ● Summary DATA 10 Copyright 2019 by Xiaoyu Li

10 Copyright © 2019 by Xiaoyu Li. Association Rules

What are Association Rules? .Association rule mining: *"Finding frequent patterns,associations, correlations,or causal structures among sets of items or objects in transactional databases,relational databases,and other information repositories." ·Applications: 米 market basket data analysis,cross-marketing, catalog design,loss-leader analysis,etc. DATA 11 Copyright 2019 by Xiaoyu Li

11 Copyright © 2019 by Xiaoyu Li. What are Association Rules?

Properties of Association Rules Express how items or objects are related to each other,and how they tend to group together. Simple to understand (comprehensibility). Provide useful information (utilizability) Efficient discovery algorithms exist (efficiency). 12 DATA Copyright 2019 by Xiaoyu Li

12 Copyright © 2019 by Xiaoyu Li. Properties of Association Rules

Analysis of Example 1 Analysis of customers buying habits by finding associations between the different items that customers place in their "shopping baskets". Customer 1 Customer 3 Milk,eggs, cereal,bread Milk,eggs, Eggs,sugar sugar,bread Customer 2 13 DATA Copyright 2019 by Xiaoyu Li

13 Copyright © 2019 by Xiaoyu Li. Analysis of Example 1