AE SemRL: Learning Semantic Association Rules with Autoencoders

arXiv:2403.18133v1 Announce Type: new
Abstract: Association Rule Mining (ARM) is the task of learning associations among data features in the form of logical rules. Mining association rules from high-dimensional numerical data, for example, time series data from a large number of sensors in a smart environment, is a computationally intensive task. In this study, we propose an Autoencoder-based approach to learn and extract association rules from time series data (AE SemRL). Moreover, we argue that in the presence of semantic …

abstract arm arxiv association autoencoders cs.ai cs.lg data data features environment example features form mining numerical rules semantic sensors series smart time series type