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This PhD research formulates the problem of mining contextual behavioral rules for individual users utilizing their smartphone data. In particular, we propose a dynamic time-series segmentation technique for behavioral data clustering, a machine learning rule-based classification technique for context-aware usage behavior modeling and prediction, and a recency-based rule learning approach for dynamic updating and management of the discovered behavioral rules, to achieve our goal.
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