Association Discovery is an unsupervised Machine Learning task used to find out meaningful relationships between values in high-dimensional datasets. Some popular use cases are market basket analysis (e.g., discovering which products customers buy together), recommendations, web usage pattern analysis, bioinformatics, incident detection, and digital forensics, among others.
The BigML associations' algorithm was acquired from Professor Geoff Webb (Monash University), a globally acknowledged expert, who spent ten years developing the association discovery in Magnum Opus.
This video shows how to easily find statistically significant rules in your data using BigML associations through the BigML Dashboard:
For more details, please find the documentation about associations here if you are using the Dashboard, or here if you prefer the API.