Association Rule Mining: Using Apriori to Uncover Shopping Patterns(Practical Data Analysis 24)
Learn association rule mining with Apriori! Discover shopping patterns, understand support, confidence, and lift, and explore the efficient FP-Growth algorithm.
Welcome to the "Practical Data Analysis" Series.
Today, I will guide you through learning about association rule mining. The concept of association rules was first proposed by Agrawal et al. in 1993.
In 1994, Agrawal et al. introduced the Apriori algorithm, which is based on association rules. To this day, it remains an important algorithm for association rule mining.
Association rule mining allows us to discover relationships between items in a dataset. It has many applications in our daily lives, with "market basket analysis" being a common example. This approach helps uncover relationships between products based on consumer transaction records, enabling strategies such as product bundling or recommendations to boost sales.
Therefore, association rule mining is a highly useful technique. In today's session, I hope you can approach it with questions in mind as we explore the following key concepts together:
Understanding Key Concepts in Association Rules:
Support, Confidence, and Lift;
The working principle of the Apriori algorithm;
How to perform association rule mining in practical applications.