This work explores a method for classifying peaks appearing within a data-intensive time-series. We summarize a case study from a clinical trial aimed at reducing secondhand smoke exposure via the installation of air particle monitors in households. Proper orthogonal decomposition (POD) in conjunction with a k-means clustering algorithm assigns each data peak to one of two clusters. Aversive feedback from the monitors increased the proportion of short-duration, attenuated peaks from 38.8% to 96.6%. For each cluster, a distribution of parameters from a physics-based model of airborne particles is estimated. Peaks generated from these distributions are correctly identified by POD/clustering with >60% accuracy.
Berardi, V., Carretero-González, R., Klepeis, N. E., et al. (2015). Proper orthogonal decomposition methods for air particle time-series in residences: Exploring peak clustering by occupant behavior patterns. Journal of Computational Science, 11, 102-111. doi: 10.1016/j.jocs.2015.10.006
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