AE SemRL: Learning Semantic Association Rules with Autoencoders
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917623693836288 |
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| author | Karabulut, Erkan Degeler, Victoria Groth, Paul |
| author_facet | Karabulut, Erkan Degeler, Victoria Groth, Paul |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_18133 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AE SemRL: Learning Semantic Association Rules with Autoencoders Karabulut, Erkan Degeler, Victoria Groth, Paul Machine Learning Artificial Intelligence 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. |
| title | AE SemRL: Learning Semantic Association Rules with Autoencoders |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2403.18133 |