  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 information related to time series data sources,
semantics can facilitate learning generalizable and explainable association
rules. Despite enriching time series data with additional semantic features, AE
SemRL makes learning association rules from high-dimensional data feasible. Our
experiments show that semantic association rules can be extracted from a latent
representation created by an Autoencoder and this method has in the order of
hundreds of times faster execution time than state-of-the-art ARM approaches in
many scenarios. We believe that this study advances a new way of extracting
associations from representations and has the potential to inspire more
research in this field.
