Ambient Assisted Living promotes healthy independent ageing of the elderly at their homes by monitoring their behaviour, and support medical assistance whenever needed. For privacy and acceptance issues, non-intrusive sensors are preferably used. However, such sensors are more prone to produce false positive or negative data. Faulty sensor data could be automatically detected if correlations between sensors can be identified. This paper aims to propose the use of association rule mining to find correlations between binary event-driven sensors installed for monitoring purposes in an apartment. A case study was carried out to validate the approach and investigate the effect of different data mining parameters on the quality of obtained association rules. The results show that correlations could be successfully deduced from unlabelled datasets with no prior expert knowledge on the sensors topology.
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