Conformal Association Rule Mining (CARM): A novel technique for data error detection and probabilistic correction

Conformal prediction (CP) is a modern framework for reliable machine learning. It is most com- monly used in the context of supervised learning, where in combination with an underlying algo- rithm it generates predicted labels for new, unlabelled examples and complements each of them with an individual measure of confidence. Conversely, association rule mining (ARM) is an unsu- pervised learning technique for discovering interesting relationships in large datasets in the form of rules. In this work, we integrate CP and ARM to develop a novel technique termed Confor- mal Association Rule Mining (CARM). The technique enables the identification of probable errors within a set of binary labels. Subsequently, these probable errors are analysed using another modern framework called Venn-ABERS prediction to correct the value in a probabilistic way.