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RobustSPAM for Inference from Noisy Longitudinal Data and Preservation of Privacy
2017
2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)
The availability of complex temporal datasets in social, health and consumer contexts has driven the development of pattern mining techniques that enable the use of classical machine learning tools for model building. In this work we introduce a robust temporal pattern mining framework for finding predictive patterns in complex timestamped multivariate and noisy data. We design an algorithm RobustSPAM that enables mining of temporal patterns from data with noisy timestamps. We apply our
doi:10.1109/icmla.2017.0-137
dblp:conf/icmla/PalczewskaPAK17
fatcat:teuveqopsjgkza73t654l7edke