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 here on Github.
Reference
Finding significant combinations of features in the presence of categorical covariates
Laetitia Papaxanthos*, Felipe Llinares-López*, Dean Bodenham and Karsten Borgwardt (*=equal contributions)
Advances in Neural Information Processing Systems 29 (NIPS 2016), 2271-2279.
Online | ETH Research Collection | Project page | GitHub
Contact FelipeLlinares-López for questions regarding usage or reporting bugs.