The most common application of association rule learning is in the analysis of transactional data, where the goal is to uncover associations between different items purchased together. This is often referred to as market basket analysis.
The key characteristics of association rule learning include:
- No Labeled Output: Association rule learning operates on datasets where items or variables are present, but there are no explicit labels or target values.
- Discovering Patterns: The algorithm aims to discover patterns that indicate co-occurrence or relationships between variables, revealing insights about the structure of the data.
- Support and Confidence Measures: Association rules are typically evaluated based on support and confidence measures, helping identify strong associations between items.
Common algorithms used for association rule learning include the Apriori algorithm and the FP-growth algorithm. These algorithms analyze transactional data to extract rules that describe the relationships between items.
Go back to Unsupervised Learning