Significant Pattern Mining with Covariates (FACS)

Summary

In this project, we developed the first approach to significant discriminative itemset mining that allows one to correct for a confounding categorical covariate.

We propose the Fast Automatic Conditional Search (FACS) algorithm, a significant discriminative itemset mining method which conditions on categorical covariates and only scales as O(k log k), where k is the number of states of the categorical covariate. Based on the Cochran-Mantel-Haenszel Test, FACS demonstrates superior speed and statistical power on simulated and real-world datasets compared to the state of the art, opening the door to numerous applications in biomedicine.

The following short spotlight video also summarizes the main aspects of the paper:

Code

The code is available external pageherecall_made on Github.

Reference