Downloaded 2,498 times
AO
Uploaded byAlbert Orriols-Puig
43,047 views
Lecture13 - Association Rules
This document provides an introduction to association rule mining. It begins with an overview of association rule mining and its application to market basket analysis. It then discusses key concepts like support, confidence and interestingness of rules. The document introduces the Apriori algorithm for mining association rules, which works in two steps: 1) generating frequent itemsets and 2) generating rules from frequent itemsets. It provides examples of how Apriori works and discusses challenges in association rule mining like multiple database scans and candidate generation.
In this document
Powered by AI
Overview of Machine Learning and introduction to Association Rules along with a recap of previous lectures.
Explains association rules through market basket analysis finding relationships among items bought together.
Defines frequent itemsets, support count calculations, and measurement of interestingness of rules.
Introduction to binary, quantitative, and fuzzy association rules, laying groundwork for further exploration.
Details the Apriori algorithm's stepwise process for generating frequent itemsets using the downward closure property.
Illustration of itemset generation for association rules and an example of minimum support calculation.
Details the iterative steps of the Apriori algorithm for generating frequent itemsets and using them for rule creation.
Discusses generating rules from frequent itemsets, exploring efficiency in the rule generation process.
Addresses challenges in association rule mining, highlighting issues with multiple database scans and candidate management.
An introduction to advanced topics that will be discussed in the next class regarding association rule mining.
